Using R in microbial analysis-2
Tao Wen(文涛)
2023-05-09
make phyloseq object (构建phyloseq对象)
# Reading the raw file（原始文件读取）
metadata = read.delim("./data/data.ps/metadata.tsv")
row.names(metadata) = metadata$SampleID
otutab = read.table("./data/data.ps/otutab.txt", header=T, row.names=1, sep="\t", comment.char="", stringsAsFactors = F)
taxonomy = read.table("./data/data.ps/taxonomy.txt", header=T, row.names=1, sep="\t", comment.char="", stringsAsFactors = F)
library(ggtree)
tree = read.tree("./data/data.ps/otus.tree")
library(Biostrings)
rep = readDNAStringSet("./data/data.ps/otus.fa")
# Import phyloseq package（导入phyloseq(ps)R包）
library(phyloseq)
ps = phyloseq(sample_data(metadata),
otu_table(as.matrix(otutab), taxa_are_rows=TRUE),
tax_table(as.matrix(taxonomy)),
phy_tree(tree),
refseq(rep)
)
ps
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 2432 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 11 sample variables ]
#> tax_table() Taxonomy Table: [ 2432 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 2432 tips and 2431 internal nodes ]
#> refseq() DNAStringSet: [ 2432 reference sequences ]
saveRDS(ps,"./data/data.ps/make.ps.rds")Preparing data analysis (数据分析准备）
Import data (ps object) and R packages（导入数据(ps对象)，R包）
# BiocManager::install("ggthemes")
library(tidyverse)
library(phyloseq)
#--输入和设定构建好的ps文件#-----
ps0 = base::readRDS("./data/dataNEW/ps_16s.rds")
#-扩增子分析需要导入的R包#-----------
library(ggClusterNet)
library(EasyStat)
library(fs)
library(ggthemes)
library(RColorBrewer)#调色板调用包
library(magrittr)
library(MicrobiotaProcess)
library(ggsignif)
library(ggtree)
library(ggtreeExtra)
library(ggstar)
library(MicrobiotaProcess)
library(ggnewscale)
library(grid)
# 建立结构保存一级目录#--------
result_path <- paste("./","/result_and_plot/",sep = "")
fs::dir_create(result_path)
res1path <- paste(result_path,"/Base_diversity_16s",sep = "")
fs::dir_create(res1path)
#-设定两个所用主题
mytheme1 = ggplot2::theme_bw() + ggplot2::theme(
panel.background= ggplot2::element_blank(),
panel.grid= ggplot2::element_blank(),
legend.position="right",
legend.title = ggplot2::element_blank(),
legend.background= ggplot2::element_blank(),
legend.key= ggplot2::element_blank(),
plot.title = ggplot2::element_text(vjust = -8.5,hjust = 0.1),
axis.title.y = ggplot2::element_text(colour = "black"),
axis.title.x = ggplot2::element_text(),
axis.text = ggplot2::element_text(),
axis.text.x = ggplot2::element_text(),
axis.text.y = ggplot2::element_text(),
legend.text = ggplot2::element_text()
)
mytheme2 = ggplot2::theme_bw() + ggplot2::theme(
panel.background= ggplot2::element_blank(),
panel.grid= ggplot2::element_blank(),
legend.position="right",
legend.title = ggplot2::element_blank(),
legend.background= ggplot2::element_blank(),
legend.key= ggplot2::element_blank(),
plot.title = ggplot2::element_text(vjust = -8.5,hjust = 0.1),
axis.title.y = ggplot2::element_text(),
axis.title.x = ggplot2::element_text(),
axis.text = ggplot2::element_text(),
axis.text.x = ggplot2::element_text(angle = 90),
axis.text.y = ggplot2::element_text(),
legend.text = ggplot2::element_text()
)
#---扩增子环境布置
colset1 <- RColorBrewer::brewer.pal(9,"Set1")
colset2 <- RColorBrewer::brewer.pal(12,"Paired")
colset3 <- c(RColorBrewer::brewer.pal(11,"Set1"),RColorBrewer::brewer.pal(9,"Pastel1"))
colset4 = colset3
ps = ps0
#--提取有多少个分组#-----------
gnum = phyloseq::sample_data(ps)$Group %>% unique() %>% length()
gnum
#> [1] 3
# 设定排序顺序
axis_order = phyloseq::sample_data(ps)$Group %>%unique();axis_order
#> [1] "Group1" "Group2" "Group3"
ps = ps0
ps_biost = ggClusterNet::filter_OTU_ps(ps = ps,Top = 500)
#--R 语言做lefse法分析-过滤#----------
ps_Rlefse = ggClusterNet::filter_OTU_ps(ps = ps,Top = 400)
#-------功能预测#----
if (is.null(ps@refseq)) {
Tax4Fun = FALSE
} else if(!is.null(ps@refseq)){
Tax4Fun = TRUE
}
ps.t = ps %>% ggClusterNet::filter_OTU_ps(500)
if (Tax4Fun) {
dir.create("data")
otu = ps.t %>%
# ggClusterNet::filter_OTU_ps(1000) %>%
ggClusterNet:: vegan_otu() %>%
t() %>%
as.data.frame()
# write.table(otu,"./data/otu.txt",quote = F,row.names = T,col.names = T,sep = "\t")
rep = ps.t %>%
# ggClusterNet::filter_OTU_ps(1000) %>%
phyloseq::refseq()
rep
# library(Biostrings)
Biostrings::writeXStringSet(rep,"./data/otu.fa")
ps.t = ps.t
#开头空一格字符保存
write.table("\t", "./data/otu.txt",append = F, quote = F, eol = "", row.names = F, col.names = F)
# 保存统计结果，有waring正常
write.table(otu, "./data/otu.txt", append = T, quote = F, sep="\t", eol = "\n", na = "NA", dec = ".", row.names = T, col.names = T)
}
#-构建保存路径
otupath = paste(res1path,"/OTU/",sep = "");otupath
#> [1] ".//result_and_plot//Base_diversity_16s/OTU/"
dir.create(otupath)Output basic information table（基础信息表格输出）
#--基本表格保存#----------
tabpath = paste(otupath,"/report_table/",sep = "")
dir.create(tabpath)
#--raw otu tab
otu = as.data.frame(t(ggClusterNet::vegan_otu(ps0)))
head(otu)FileName <- paste(tabpath,"/otutab.csv", sep = "")
write.csv(otu,FileName,sep = "")
# tax table
tax = as.data.frame((ggClusterNet::vegan_tax(ps0)))
head(tax)FileName <- paste(tabpath,"/tax.csv", sep = "")
write.csv(otu,FileName,sep = "")
ps0_rela = phyloseq::transform_sample_counts(ps0, function(x) x / sum(x) );ps0_rela
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq() DNAStringSet: [ 37178 reference sequences ]
#--norm otu tab
otu_norm = as.data.frame(t(ggClusterNet::vegan_otu(ps0_rela)))
FileName <- paste(tabpath,"/otutab_norm.csv", sep = "")
write.csv(otu_norm,FileName,sep = "")
otutax <- cbind(as.data.frame(t(ggClusterNet::vegan_otu(ps0_rela))),as.data.frame((ggClusterNet::vegan_tax(ps0_rela))))
FileName <- paste(tabpath,"/otutax_norm.csv", sep = "")
write.csv(otutax,FileName,sep = "")
for (i in 2: length(phyloseq::rank_names(ps0))) {
psi <- ggClusterNet::tax_glom_wt(ps = ps0,ranks = phyloseq::rank_names(ps0)[i])
#--raw otu tab
otu = as.data.frame(t(ggClusterNet::vegan_otu(psi)))
FileName <- paste(tabpath,"/otutab",phyloseq::rank_names(ps0)[i],".csv", sep = "")
write.csv(otu,FileName,sep = "")
# tax table
tax = as.data.frame((ggClusterNet::vegan_tax(ps0)))
FileName <- paste(tabpath,"/tax",phyloseq::rank_names(ps0)[i],".csv", sep = "")
write.csv(otu,FileName,sep = "")
psi_rela = phyloseq::transform_sample_counts(psi, function(x) x / sum(x) );psi_rela
#--norm otu tab
otu_norm = as.data.frame(t(ggClusterNet::vegan_otu(psi_rela)))
FileName <- paste(tabpath,"/otutab_norm",phyloseq::rank_names(psi)[i],".csv", sep = "")
write.csv(otu_norm,FileName,sep = "")
otutax <- cbind(as.data.frame(t(ggClusterNet::vegan_otu(psi_rela))),as.data.frame((ggClusterNet::vegan_tax(psi_rela))))
FileName <- paste(tabpath,"/otutax_norm",phyloseq::rank_names(ps0)[i],".csv", sep = "")
write.csv(otutax,FileName,sep = "")
}1.Community diversity analysis（1.群落多样性分析）
alpha diversity（alpha多样性）
alppath = paste(otupath,"/alpha/",sep = "")
dir.create(alppath)
#---------多种指标alpha多样性分析加出图-标记显著性
group = "Group"
# 设定排序顺序
axis_order = phyloseq::sample_data(ps)$Group %>%unique();axis_order
#> [1] "Group1" "Group2" "Group3"
samplesize = min(phyloseq::sample_sums(ps))
if (samplesize == 0) {
print("0 number sequence of some samples")
print("median number were used")
ps11 = phyloseq::rarefy_even_depth(ps,sample.size = samplesize)
} else{
ps11 = phyloseq::rarefy_even_depth(ps,sample.size = samplesize)
}
mapping = phyloseq::sample_data(ps11)
ps11 = phyloseq::filter_taxa(ps11, function(x) sum(x ) >0 , TRUE); ps11
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 27637 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 27637 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 27637 tips and 27635 internal nodes ]
#> refseq() DNAStringSet: [ 27637 reference sequences ]
# head(mapping)
colnames(mapping) = gsub(group,"AA", colnames(mapping))
mapping$Group = mapping$AA
mapping$Group = as.factor(mapping$Group)
# mapping$Group
count = as.data.frame(t(ggClusterNet::vegan_otu(ps11)))
alpha=vegan::diversity(count, "shannon")
x = t(count) ##转置，行为样本，列为OTU
# head(x)
Shannon = vegan::diversity(x) ##默认为shannon
Shannon
#> sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16
#> 7.683445 7.540214 7.466142 7.694121 7.933365 7.931841 7.901167 7.873805
#> sample17 sample18 sample2 sample3 sample4 sample5 sample6 sample7
#> 7.926233 7.923863 7.595181 7.687760 7.895519 7.966725 7.599759 7.254086
#> sample8 sample9
#> 7.424512 6.864458
Inv_Simpson <- vegan::diversity(x, index = "invsimpson")
Inv_Simpson
#> sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16
#> 653.4143 442.9412 478.1011 454.2344 908.2110 828.0545 878.2087 805.3710
#> sample17 sample18 sample2 sample3 sample4 sample5 sample6 sample7
#> 854.2719 871.1567 737.7287 708.5443 907.1260 898.3528 596.7137 383.6056
#> sample8 sample9
#> 186.1661 80.6827
#计算OTU数量
S <- vegan::specnumber(x);S ##每个样本物种数。等价于S2 = rowSums(x>0)
#> sample1 sample10 sample11 sample12 sample13 sample14 sample15 sample16
#> 7819 7027 6853 8772 10025 10358 9891 10035
#> sample17 sample18 sample2 sample3 sample4 sample5 sample6 sample7
#> 10525 10311 5513 6032 8797 10203 6024 4581
#> sample8 sample9
#> 7882 5271
S2 = rowSums(x>0)
#多样性指标：均匀度Pielou_evenness，Simpson_evenness
Pielou_evenness <- Shannon/log(S)
Simpson_evenness <- Inv_Simpson/S
est <- vegan::estimateR(x)
est <- vegan::estimateR(x)
Richness <- est[1, ]
Chao1 <- est[2, ]
ACE <- est[4, ]
report = cbind(Shannon, Inv_Simpson, Pielou_evenness, Simpson_evenness,
Richness, Chao1,ACE)
head(report)
#> Shannon Inv_Simpson Pielou_evenness Simpson_evenness Richness
#> sample1 7.683445 653.4143 0.8571148 0.08356750 7819
#> sample10 7.540214 442.9412 0.8512787 0.06303418 7027
#> sample11 7.466142 478.1011 0.8453090 0.06976522 6853
#> sample12 7.694121 454.2344 0.8474336 0.05178230 8772
#> sample13 7.933365 908.2110 0.8611207 0.09059462 10025
#> sample14 7.931841 828.0545 0.8579124 0.07994348 10358
#> Chao1 ACE
#> sample1 8426.818 8317.225
#> sample10 7427.908 7359.968
#> sample11 7326.264 7223.793
#> sample12 9450.131 9358.131
#> sample13 11019.921 10966.996
#> sample14 11370.823 11377.163
alp = merge(mapping,report , by="row.names",all=F)
index = c("Shannon","Inv_Simpson","Pielou_evenness","Simpson_evenness" ,"Richness" ,"Chao1","ACE" )
#--多种组合alpha分析和差异分析出图
index= alp
# head(index)
#--提取三个代表指标作图
sel = c(match("Shannon",colnames(index)),match("Richness",colnames(index)),match("Pielou_evenness",colnames(index)))
data = cbind(data.frame(ID = 1:length(index$Group),group = index$Group),index[sel])
# head(data)
result = EasyStat::MuiaovMcomper2(data = data,num = c(3:5))
FileName <- paste(alppath,"/alpha_diversity_different_label.csv", sep = "")
write.csv(result,FileName,sep = "")
FileName <- paste(alppath,"/alpha_diversity_index.csv", sep = "")
write.csv(index,FileName,sep = "")
result1 = EasyStat::FacetMuiPlotresultBox(data = data,num = c(3:5),
result = result,
sig_show ="abc",ncol = 3 )
p1_1 = result1[[1]] +
ggplot2::scale_x_discrete(limits = axis_order) +
mytheme1 +
ggplot2::guides(fill = guide_legend(title = NULL)) +
ggplot2::scale_fill_manual(values = colset1)
p1_1
#如何升级展示-提取数据用小提琴图展示
p1_1 = result1[[2]] %>% ggplot(aes(x=group , y=dd )) +
geom_violin(alpha=1, aes(fill=group)) +
geom_jitter( aes(color = group),position=position_jitter(0.17), size=3, alpha=0.5)+
labs(x="", y="")+
facet_wrap(.~name,scales="free_y",ncol = 3) +
# theme_classic()+
geom_text(aes(x=group , y=y ,label=stat)) +
ggplot2::scale_x_discrete(limits = axis_order) +
mytheme1 +
guides(color=guide_legend(title = NULL),
shape=guide_legend(title = NULL),
fill = guide_legend(title = NULL)
) +
ggplot2::scale_fill_manual(values = colset1)
p1_1
res = EasyStat::FacetMuiPlotresultBar(data = data,num = c(3:5),result = result,sig_show ="abc",ncol = 3)
p1_2 = res[[1]]+ scale_x_discrete(limits = axis_order) + guides(color = FALSE) +
mytheme1+
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
p1_2
res = EasyStat::FacetMuiPlotReBoxBar(data = data,num = c(3:5),result = result,sig_show ="abc",ncol = 3)
p1_3 = res[[1]]+ scale_x_discrete(limits = axis_order) +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
p1_3
FileName <- paste(alppath,"Alpha_Facet_box", ".pdf", sep = "")
ggsave(FileName, p1_1, width = ((1 + gnum) * 3), height =4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_bar", ".pdf", sep = "")
ggsave(FileName, p1_2, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_boxbar", ".pdf", sep = "")
ggsave(FileName, p1_3, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_box", ".jpg", sep = "")
ggsave(FileName, p1_1, width = ((1 + gnum) * 3), height =4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_bar", ".jpg", sep = "")
ggsave(FileName, p1_2, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)
FileName <- paste(alppath,"Alpha_Facet_boxbar", ".jpg", sep = "")
ggsave(FileName, p1_3, width = ((1 + gnum) * 3), height = 4,limitsize = FALSE)
#--总体差异检测alpha多样性
krusk1 = ggpubr::compare_means( Shannon ~ group, data=data, method = "kruskal.test")
krusk2 = ggpubr::compare_means( Richness ~ group, data=data, method = "kruskal.test")
krusk3 = ggpubr::compare_means( Pielou_evenness ~ group, data=data, method = "kruskal.test")
dat = rbind(krusk1,krusk2,krusk3) %>% as.data.frame()
FileName <- paste(alppath,"/alpha_diversity_index_all_p_Kruskal-Wallis.csv", sep = "")
write.csv(dat,FileName,sep = "")alpha-rarefaction curve（alpha稀释曲线）
rare <- mean(phyloseq::sample_sums(ps))/10
method = "Richness"
group = "Group"
start = 100
step = 3000
# library(microbiome)
phyRare = function(ps = ps,N = 3000){
otb = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
otb1 = vegan::rrarefy(t(otb), N)
ps = phyloseq::phyloseq(phyloseq::otu_table(as.matrix(otb1),taxa_are_rows = F),
phyloseq::sample_data(ps)
)
return(ps)
}
#---- 全部指标#----
all = c("observed" , "chao1" , "diversity_inverse_simpson" , "diversity_gini_simpson",
"diversity_shannon" , "diversity_fisher" , "diversity_coverage" , "evenness_camargo",
"evenness_pielou" , "evenness_simpson" , "evenness_evar" , "evenness_bulla",
"dominance_dbp" , "dominance_dmn" , "dominance_absolute" , "dominance_relative",
"dominance_simpson" , "dominance_core_abundance" , "dominance_gini" , "rarity_log_modulo_skewness",
"rarity_low_abundance" , "rarity_noncore_abundance", "rarity_rare_abundance")
#--- 运行计算#----
for (i in seq(start,max(phyloseq::sample_sums(ps)), by = step) ) {
psRe = phyRare(ps = ps, N = i)
if (method == "Richness") {
count = as.data.frame(t(ggClusterNet::vegan_otu(psRe)))
# head(count)
x = t(count) ##转置，行为样本，列为OTU
est = vegan::estimateR(x)
index = est[1, ]
}
if (method %in% c("ACE")) {
ap_phy = phyloseq::estimate_richness(psRe, measures =method)
# head(ap_phy)
index = ap_phy$ACE
}
if (method %in% all) {
alp_mic = microbiome::alpha(psRe,index=method)
# head(alp_mic)
index = alp_mic[,1]
}
tab = data.frame(ID = names(phyloseq::sample_sums(psRe)))
#得到多样性的列
tab = cbind(tab,i,index)
# head(tab)
if (i == start) {
result = tab
}
if (i != start) {
result = rbind(result,tab)
}
}
for (ii in 1:length(phyloseq::sample_sums(ps))) {
result$i[result$i > phyloseq::sample_sums(ps)[ii][[1]]]
df_filter= filter(result, ID ==names(phyloseq::sample_sums(ps)[ii]) &i > phyloseq::sample_sums(ps)[ii][[1]])
result$index
result$index[result$i>phyloseq::sample_sums(ps)[ii][[1]]]
a = result$i>phyloseq::sample_sums(ps)[ii][[1]]
a[a == FALSE] = "a"
b = result$ID == names(phyloseq::sample_sums(ps)[ii])
b[b == FALSE] = "b"
result$index[a== b] = NA
}
#----直接给列，添加分组#----
map = as.data.frame(phyloseq::sample_data(ps))
result$Group = map$Group
## 绘制稀释曲线
main_theme =theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.title = element_text(vjust = -8.5,hjust = 0.1),
axis.title.y =element_text(size = 7,face = "bold",colour = "black"),
axis.title.x =element_text(size = 7,face = "bold",colour = "black"),
axis.text = element_text(size = 7,face = "bold"),
axis.text.x = element_text(colour = "black",size = 7),
axis.text.y = element_text(colour = "black",size = 7),
legend.text = element_text(size = 7,face = "bold")
)
head(result) result = result %>% dplyr::filter(index != "NA")
result$ID = as.factor(result$ID)
p = ggplot(data= result,aes(x = i,y = index,group = ID,color = Group)) +
geom_line() +
labs(x= "",y=method,title="") +theme_bw()+main_theme
p
#---分组求均值和标准误+se#---
data = result
groups= dplyr::group_by(data, Group,i)
data2 = dplyr::summarise(groups , mean(index), sd(index))
# head(data2)
colnames(data2) = c(colnames(data2)[1:2],"mean","sd")
# 按组均值绘图
p2 = ggplot(data= data2,aes(x = i,y = mean,colour = Group)) +
geom_line()+
labs(x= "",y=method,title="") +theme_bw()+main_theme
# p2
# 组均值+标准差
p4 = ggplot(data=data2,aes(x = i,y = mean,colour = Group)) +
geom_errorbar(data = data2,aes(ymin=mean - sd, ymax=mean + sd,colour = Group),alpha = 0.4, width=.1)+labs(x= "",y=method,title="") +theme_bw()+main_theme
p4
# return(list(p,table = result,p2,p4))
#--提供单个样本溪稀释曲线的绘制
p2_1 <- p +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
## 提供数据表格，方便输出
raretab <- result
head(raretab)#--按照分组展示稀释曲线
p2_2 <- p2 +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
#--按照分组绘制标准差稀释曲线
p2_3 <- p4 +
mytheme1 +
guides(fill = guide_legend(title = NULL))+
scale_fill_manual(values = colset1)
FileName <- paste(alppath,"Alpha_rare_sample", ".pdf", sep = "")
ggsave(FileName, p2_1, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_group", ".pdf", sep = "")
ggsave(FileName, p2_2, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_groupwithSD", ".pdf", sep = "")
ggsave(FileName, p2_3, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_sample", ".jpg", sep = "")
ggsave(FileName, p2_1, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_group", ".jpg", sep = "")
ggsave(FileName, p2_2, width = 8, height =6)
FileName <- paste(alppath,"Alpha_rare_groupwithSD", ".jpg", sep = "")
ggsave(FileName, p2_3, width = 8, height =6)
FileName <- paste(alppath,"/Alpha_rare_data.csv", sep = "")
write.csv(raretab,FileName,sep = "")beta diversity（beta多样性）
betapath = paste(otupath,"/beta/",sep = "")
dir.create(betapath)
group = "Group"
dist = "bray"
method ="PCoA"
Micromet = "adonis"
pvalue.cutoff = 0.05
pair=TRUE
# 求取相对丰度#----
ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )
# 排序方法选择#----
#---------如果选用DCA排序
if (method == "DCA") {
# method = "DCA"
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#提取样本坐标
points = ordi$rproj[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取特征值
eig = ordi$evals^2
}
#---------CCA排序#----
if (method == "CCA") {
# method = "CCA"
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#样本坐标,这里可选u或者v矩阵
points = ordi$CA$v[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取特征值
eig = ordi$CA$eig^2
}
#---------RDA排序#----
if (method == "RDA") {
# method ="RDA"
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#样本坐标,这里可选u或者v矩阵
points = ordi$CA$v[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取特征值
eig = ordi$CA$eig
}
#---------DPCoA排序#----
# 不用做了，不选择这种方法了，这种方法运行太慢了
#---------MDS排序#----
if (method == "MDS") {
# method = "MDS"
# ordi = ordinate(ps1_rela, method=ord_meths[i], distance=dist)
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#样本坐标
points = ordi$vectors[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取解释度
eig = ordi$values[,1]
}
#---------PCoA排序#----
if (method == "PCoA") {
# method = "PCoA"
unif = phyloseq::distance(ps1_rela , method=dist, type="samples")
#这里请记住pcoa函数
pcoa = stats::cmdscale(unif, k=2, eig=T) # k is dimension, 3 is recommended; eig is eigenvalues
points = as.data.frame(pcoa$points) # 获得坐标点get coordinate string, format to dataframme
colnames(points) = c("x", "y") #命名行名
eig = pcoa$eig
}
#----PCA分析#----
otu_table = as.data.frame(t(ggClusterNet::vegan_otu(ps1_rela )))
# head(otu_table)
# method = "PCA"
if (method == "PCA") {
otu.pca = stats::prcomp(t(otu_table), scale.default = TRUE)
#提取坐标
points = otu.pca$x[,1:2]
colnames(points) = c("x", "y") #命名行名
# #提取荷载坐标
# otu.pca$rotation
# 提取解释度,这里提供的并不是特征值而是标准差，需要求其平方才是特征值
eig=otu.pca$sdev
eig=eig*eig
}
# method = "LDA"
#---------------LDA排序#----
if (method == "LDA") {
#拟合模型
# library(MASS)
data = t(otu_table)
# head(data)
data = as.data.frame(data)
# data$ID = row.names(data)
data = scale(data, center = TRUE, scale = TRUE)
dim(data)
data1 = data[,1:10]
map = as.data.frame(sample_data(ps1_rela))
model = MASS::lda(data, map$Group)
# 提取坐标
ord_in = model
axes = c(1:2)
points = data.frame(predict(ord_in)$x[, axes])
colnames(points) = c("x", "y") #命名行名
# 提取解释度
eig= ord_in$svd^2
}
#---------------NMDS排序#----
# method = "NMDS"
if (method == "NMDS") {
#---------如果选用NMDS排序
# i = 5
# dist = "bray"
ordi = phyloseq::ordinate(ps1_rela, method=method, distance=dist)
#样本坐标,
points = ordi$points[,1:2]
colnames(points) = c("x", "y") #命名行名
#提取stress
stress = ordi$stress
stress= paste("stress",":",round(stress,2),sep = "")
}
#---------------t-sne排序#----
# method = "t-sne"
if (method == "t-sne") {
data = t(otu_table)
# head(data)
data = as.data.frame(data)
# data$ID = row.names(data)
#
data = scale(data, center = TRUE, scale = TRUE)
dim(data)
map = as.data.frame(sample_data(ps1_rela))
# row.names(map)
#---------tsne
# install.packages("Rtsne")
# library(Rtsne)
tsne = Rtsne::Rtsne(data,perplexity = 3)
# 提取坐标
points = as.data.frame(tsne$Y)
row.names(points) = row.names(map)
colnames(points) = c("x", "y") #命名行名
stress= NULL
}
#----差异分析#----
#----整体差异分析#----
g = sample_data(ps)$Group %>% unique() %>% length()
n = sample_data(ps)$Group%>% length()
o = n/g
source("./function/MicroTest.R")
source("./function/pairMicroTest.R")
if (o >= 3) {
title1 = MicroTest(ps = ps1_rela, Micromet = Micromet, dist = dist)
title1
} else{
title1 = NULL
}
#> [1] "Adonis:R 0.364 p: 0.001"
if (F) {
#----两两比较#----
pairResult = pairMicroTest(ps = ps1_rela, Micromet = Micromet, dist = dist)
} else {
pairResult = "no result"
}
#----绘图#----
map = as.data.frame(phyloseq::sample_data(ps1_rela))
map$Group = as.factor(map$Group)
colbar = length(levels(map$Group))
points = cbind(points, map[match(rownames(points), rownames(map)), ])
# head(points)
points$ID = row.names(points)
if (method %in% c("DCA", "CCA", "RDA", "MDS", "PCoA","PCA","LDA")) {
p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
geom_point(alpha=.7, size=5, pch = 21) +
labs(x=paste0(method," 1 (",format(100*eig[1]/sum(eig),digits=4),"%)"),
y=paste0(method," 2 (",format(100*eig[2]/sum(eig),digits=4),"%)"),
title=title1) +
stat_ellipse(linetype=2,level=0.68,aes(group=Group, colour=Group))
p3 = p2+ggrepel::geom_text_repel(aes(label=points$ID),size = 5)
p3
}
if (method %in% c("NMDS")) {
p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
geom_point(alpha=.7, size=5, pch = 21) +
labs(x=paste(method,"1", sep=""),
y=paste(method,"2",sep=""),
title=stress)+
stat_ellipse( linetype = 2,level = 0.65,aes(group =Group, colour = Group))
p3 = p2+ggrepel::geom_text_repel( aes(label=points$ID),size=4)
p3
if (method %in% c("t-sne")) {
supp_lab = labs(x=paste(method,"1", sep=""),y=paste(method,"2",sep=""),title=title)
p2 = p2 + supp_lab
p3 = p3 + supp_lab
}
eig = NULL
}
if (method %in% c("t-sne")) {
p2 =ggplot(points, aes(x=x, y=y, fill = Group)) +
geom_point(alpha=.7, size=5, pch = 21) +
labs(x=paste(method,"1", sep=""),
y=paste(method,"2",sep=""))+
stat_ellipse( linetype = 2,level = 0.65,aes(group =Group, colour = Group))
p3 = p2+ggrepel::geom_text_repel( aes(label=points$ID),size=4)
p3
if (method %in% c("t-sne")) {
supp_lab = labs(x=paste(method,"1", sep=""),y=paste(method,"2",sep=""),title=title1)
p2 = p2 + supp_lab
p3 = p3 + supp_lab
}
eig = NULL
}
p3_1 = p2 +
scale_fill_manual(values = colset1)+
scale_color_manual(values = colset1,guide = F) +
mytheme1 +
theme(legend.position = c(0.2,0.2))
p3_1
#带标签图形出图
p3_2 = p3 +
scale_fill_manual(values = colset1)+
scale_color_manual(values = colset1,guide = F) +
mytheme1 +
theme(legend.position = c(0.2,0.2))
p3_2
FileName <- paste(betapath,"/a2_",method,"bray.pdf", sep = "")
ggsave(FileName, p3_1, width = 8, height = 8)
FileName1 <- paste(betapath,"/a2_",method,"",method,"bray.jpg", sep = "")
ggsave(FileName1 , p3_1, width = 12, height = 12)
FileName <- paste(betapath,"/a2_",method,"bray_label.pdf", sep = "")
ggsave(FileName, p3_2, width = 12, height = 12)
FileName1 <- paste(betapath,"/a2_",method,"bray_label.jpg", sep = "")
ggsave(FileName1 , p3_2, width = 12, height = 12)
# 提取出图数据
plotdata = points
FileName <- paste(betapath,"/a2_",method,"bray.csv", sep = "")
write.csv(plotdata,FileName)
#---------排序-精修图
plotdata = points
head(plotdata) # 求均值
cent <- aggregate(cbind(x,y) ~Group, data = plotdata, FUN = mean)
cent # 合并到样本坐标数据中
segs <- merge(plotdata, setNames(cent, c('Group','oNMDS1','oNMDS2')),
by = 'Group', sort = FALSE)
library(ggsci)
p3_3 = p3_1 +geom_segment(data = segs,
mapping = aes(xend = oNMDS1, yend = oNMDS2,color = Group),show.legend=F) + # spiders
geom_point(mapping = aes(x = x, y = y),data = cent, size = 5,pch = 24,color = "black",fill = "yellow") +
scale_fill_manual(values = colset1)+
scale_color_manual(values = colset1,guide = F) +
mytheme1 +
theme(legend.position = c(0.2,0.2))
p3_3
FileName <- paste(betapath,"/a2_",method,"bray_star.pdf", sep = "")
ggsave(FileName, p3_3, width = 8, height = 8)
FileName1 <- paste(betapath,"/a2_",method,"bray_star.jpg", sep = "")
ggsave(FileName1 , p3_3, width = 8, height = 8)
# map
#提取总体比较
TResult = title1
head(TResult)
#> [1] "Adonis:R 0.364 p: 0.001"
FileName <- paste(betapath,"Total_anosim.csv", sep = "")
write.csv(TResult,FileName)
#---普氏分析#------
map = phyloseq::sample_data(ps)
samegroup = map$Group %>% table() %>% as.data.frame() %>% .$Freq %>% unique() %>% length() == 1
method = "spearman"
group = "Group"
ncol = 5
nrow = 2
dist <-
ggClusterNet::scale_micro(ps = ps,method = "rela") %>%
ggClusterNet::vegan_otu()%>%
vegan::vegdist(method="bray") %>%
as.matrix()
map = phyloseq::sample_data(ps)
gru = map[,group][,1] %>% unlist() %>% as.vector()
id = combn(unique(gru),2)
R_mantel = c()
p_mantel = c()
name = c()
R_pro <- c()
p_pro <- c()
plots = list()
for (i in 1:dim(id)[2]) {
id_dist <- row.names(map)[gru == id[1,i]]
dist1 = dist[id_dist,id_dist]
id_dist <- row.names(map)[gru == id[2,i]]
id_dist = id_dist[1:nrow(dist1)]
dist2 = dist[id_dist,id_dist]
mt <- vegan::mantel(dist1,dist2,method = method)
R_mantel[i] = mt$statistic
p_mantel[i] = mt$signif
name[i] = paste(id[1,i],"_VS_",id[2,i],sep = "")
#--p
mds.s <- vegan::monoMDS(dist1)
mds.r <- vegan::monoMDS(dist2)
pro.s.r <- vegan::protest(mds.s,mds.r)
R_pro[i] <- pro.s.r$ss
p_pro[i] <- pro.s.r$signif
Y <- cbind(data.frame(pro.s.r$Yrot), data.frame(pro.s.r$X))
X <- data.frame(pro.s.r$rotation)
Y$ID <- rownames(Y)
p1 <- ggplot(Y) +
geom_segment(aes(x = X1, y = X2, xend = (X1 + MDS1)/2, yend = (X2 + MDS2)/2),
arrow = arrow(length = unit(0, 'cm')),
color = "#B2182B", size = 1) +
geom_segment(aes(x = (X1 + MDS1)/2, y = (X2 + MDS2)/2, xend = MDS1, yend = MDS2),
arrow = arrow(length = unit(0, 'cm')),
color = "#56B4E9", size = 1) +
geom_point(aes(X1, X2), fill = "#B2182B", size = 4, shape = 21) +
geom_point(aes(MDS1, MDS2), fill = "#56B4E9", size = 4, shape = 21) +
labs(title = paste(id[1,i],"-",id[2,i]," ","Procrustes analysis:\n M2 = ",
round(pro.s.r$ss,3),
", p-value = ",
round(pro.s.r$signif,3),
"\nMantel test:\n r = ",
round(R_mantel[i],3),
", p-value =, ",
round(p_mantel[i],3),sep = "") ) + mytheme1
p1
plots[[i]] = p1
}
dat = data.frame(name,R_mantel,p_mantel,R_pro,p_pro )
pp = ggpubr::ggarrange(plotlist = plots, common.legend = TRUE, legend="right",ncol = ncol,nrow = nrow)
mytheme1 = theme_classic() + theme(
panel.background=element_blank(),
panel.grid=element_blank(),
legend.position="right",
legend.title = element_blank(),
legend.background=element_blank(),
legend.key=element_blank(),
# legend.text= element_text(size=7),
# text=element_text(),
# axis.text.x=element_text(angle=45,vjust=1, hjust=1)
plot.title = element_text(vjust = -8.5,hjust = 0.1),
axis.title.y =element_text(size = 15,face = "bold",colour = "black"),
axis.title.x =element_text(size = 15,face = "bold",colour = "black"),
axis.text = element_text(size = 10,face = "bold"),
axis.text.x = element_text(colour = "black",size = 10),
axis.text.y = element_text(colour = "black",size = 10),
legend.text = element_text(size = 10,face = "bold")
)
#--门特尔检验-普氏分析
size = combn(unique(phyloseq::sample_data(ps)$Group),2) %>% dim()
size
#> [1] 2 3
data <- dat
p3_7 <- pp + mytheme1
p3_7
FileName <- paste(betapath,"mantel_pro.csv", sep = "")
write.csv(data,FileName)
FileName1 <- paste(betapath,"/a2_","Mantel_Pro.pdf", sep = "")
ggsave(FileName1 , p3_7, width = size[2] *6, height = 6,limitsize = FALSE)
FileName1 <- paste(betapath,"/a2_","Mantel_Pro.jpg", sep = "")
ggsave(FileName1 , p3_7, width = size[2] *6, height =6,limitsize = FALSE)Species Taxonomic Tree（物种分类树）
library(ggstar)
# library(ggtreeExtra)
library(ggtree)
# library(treeio)
# library(ggstar)
library(ggnewscale)
# #-读取进化树
# library(patchwork)
# library(ggClusterNet)
# library(phyloseq)
# library(tidyverse)
#--微生物组进化树功能
Top_micro = 150
remove_rankID = function(taxtab){
taxtab$Kingdom = gsub("d__","",taxtab$Kingdom)
taxtab$Kingdom = gsub("k__","",taxtab$Kingdom)
taxtab$Phylum = gsub("p__","",taxtab$Phylum)
taxtab$Class = gsub("c__","",taxtab$Class)
taxtab$Order = gsub("o__","",taxtab$Order)
taxtab$Family = gsub("f__","",taxtab$Family)
taxtab$Genus = gsub("g__","",taxtab$Genus)
taxtab$Species = gsub("s__","",taxtab$Species)
return(taxtab)
}
Top = 100
tax = ps %>% vegan_tax() %>%
as.data.frame()
head(tax) tax = remove_rankID(tax) %>%as.matrix()
tax[is.na(tax)] = "Unknown"
tax[tax == " "] = "Unknown"
tax_table(ps) = as.matrix(tax)
alltax = ps %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
alltax$OTU = row.names(alltax)
head(alltax)
trda <- MicrobiotaProcess::convert_to_treedata(alltax)
p0 <- ggtree::ggtree(trda, layout="inward_circular", size=0.2, xlim=c(30,NA))
# p0$data
tax = ps %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
tippoint = data.frame(OTU = row.names(tax),Taxa = tax$Phylum,Level = "Phylum")
tippoint$OTU = paste("",tippoint$OTU,sep = "")
tippoint$names <- gsub("","",tippoint$OTU)
p0_1 <- ggtree::ggtree(trda, layout="circular", size=0.2, xlim=c(30,NA)) %<+% tippoint
p0_1
p0 <- p0 %<+% tippoint
p0
a <- tippoint$Taxa %>% unique() %>% length()
b = rep(18,a)
names(b) = tippoint$Taxa %>% unique()
p1 <- p0 +
geom_tippoint(
mapping=aes(
color=Taxa,
shape=Level
),
size=1,
alpha=0.8
) +
scale_color_manual(values=colorRampPalette(RColorBrewer::brewer.pal(9,"Set1"))(a),
guide=guide_legend(
keywidth=0.5,
keyheight=0.5,
order=2,
override.aes=list(shape= b,
size=2
),
na.translate=TRUE
)
) +
scale_shape_manual(values=c("Phylum"=20, "Class"=18), guide="none" )
p1_1 <- p0_1 +
geom_tippoint(
mapping=aes(
color=Taxa,
shape=Level
),
size=1,
alpha=0.8
) +
scale_color_manual(values=colorRampPalette(RColorBrewer::brewer.pal(9,"Set1"))(a),
guide=guide_legend(
keywidth=0.5,
keyheight=0.5,
order=2,
override.aes=list(shape= b,
size=2
),
na.translate=TRUE
)
) +
scale_shape_manual(values=c("Phylum"=20, "Class"=18), guide="none" )
#------对OTU之间的关系进行连线
otu = ps %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_otu() %>%
t() %>%
as.data.frame()
head(otu) pssub = ps %>%
ggClusterNet::filter_OTU_ps(Top)
result = ggClusterNet::corMicro (ps = pssub ,N = 0,r.threshold=0.8,p.threshold=0.05,method = "pearson")
#--提取相关矩阵
cor = result[[1]]
diag(cor) = 0
# library(tidyfst)
linktab = tidyfst::mat_df(cor) %>%
dplyr::filter(value != 0) %>%
dplyr::mutate(direct = ifelse(value> 0, "positive", "nagetive"))
colnames(linktab) = c("Inhibitor","Sensitive","Interaction","direct")
head(linktab) linktab$Inhibitor = paste("st__",linktab$Inhibitor,sep = "")
linktab$Sensitive = paste("st__",linktab$Sensitive,sep = "")
head(linktab)
# p1$data
p2 <- p1 +
ggnewscale::new_scale_color() +
ggtree::geom_taxalink(
data=linktab,
mapping=aes(
taxa1=Inhibitor,
taxa2=Sensitive,
color=direct
),
alpha=0.6,
offset=0.1,
size=0.15,
ncp=10,
hratio=1,
arrow=grid::arrow(length = unit(0.005, "npc"))
) +
scale_colour_manual(values=c("chocolate2", "#3690C0", "#009E73"),
guide=guide_legend(
keywidth=0.8, keyheight=0.5,
order=1, override.aes=list(alpha=1, size=0.5)
)
)
otu$id = row.names(otu)
ringdat = otu %>% tidyfst::longer_dt(id)
ringdat$value = log2(ringdat$value+1)
ringdat$id = paste("st__",ringdat$id,sep = "")
head(ringdat) num <- ringdat$name %>% unique() %>% length()
p3 <- p2 +
geom_fruit(
data=ringdat,
geom=geom_star,
mapping=aes(
y=id,
x=name,
size=value,
fill= name
),
starshape = 13,
starstroke = 0,
offset=-0.9,
pwidth=0.8,
grid.params=list(linetype=3)
) +
scale_size_continuous(range=c(0, 2),
limits=c(sort(ringdat$value)[2], max(ringdat$value)),
breaks=c(1, 2, 3),
name=bquote(paste(Log[2],"(",.("Count+1"), ")")),
guide=guide_legend(keywidth = 0.4, keyheight = 0.4, order=4,
override.aes = list(starstroke=0.3))
) +
scale_fill_manual(
values=colorRampPalette(RColorBrewer::brewer.pal(12,"Spectral"))(num),
guide=guide_legend(keywidth = 0.4, keyheight = 0.4, order=3)
)
p4 <- p3 +
geom_tiplab(
mapping=aes(
label=names
),
align=TRUE,
size=2,
linetype=NA,
offset=16
)
map = phyloseq::sample_data(pssub)
dat <- pssub %>%
ggClusterNet::vegan_otu() %>%
as.data.frame()
bartab = cbind(dat,data.frame(row.names = row.names(map),ID = row.names(map),Group = map$Group )) %>%
dplyr::group_by(Group) %>%
dplyr::summarise_if(is.numeric,mean) %>%
as.data.frame()%>%
tidyfst::longer_dt(Group)
head(bartab) bartab$name = paste("st__",bartab$name,sep = "")
num = bartab$Group %>% unique() %>%
length()
p5 <- p4 +
ggnewscale::new_scale_fill() +
geom_fruit(
data=bartab,
geom=geom_bar,
mapping=aes(
x=value,
y=name,
fill= Group
),
stat="identity",
orientation="y",
offset=0.48,
pwidth=1.5,
axis.params=list(
axis = "x",
text.angle = -45,
hjust = 0,
vjust = 0.5,
nbreak = 4
)
) +
scale_fill_manual(
name = "Number of interactions",
values=colorRampPalette(RColorBrewer::brewer.pal(12,"Set3"))(num),
guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
) +
theme(
legend.background=element_rect(fill=NA),
legend.title=element_text(size=6.5),
legend.text=element_text(size=5),
legend.spacing.y = unit(0.02, "cm"),
legend.margin=ggplot2::margin(0.1, 0.9, 0.1,-0.9, unit="cm"),
legend.box.margin=ggplot2::margin(0.1, 0.9, 0.1, -0.9, unit="cm"),
plot.margin = unit(c(-1.2, -1.2, -1.2, 0.1),"cm")
)
p5_1 <- p1_1 +
new_scale_fill() +
geom_fruit(
data=bartab,
geom=geom_bar,
mapping=aes(
x=value,
y=name,
fill= Group
),
stat="identity",
orientation="y",
offset=0.1,
pwidth=1.5,
axis.params=list(
axis = "x",
text.angle = -45,
hjust = 0,
vjust = 0.5,
nbreak = 4
)
) +
scale_fill_manual(
name = "Number of interactions",
values=colorRampPalette(RColorBrewer::brewer.pal(12,"Set3"))(num),
guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
) +
theme(
legend.background=element_rect(fill=NA),
legend.title=element_text(size=6.5),
legend.text=element_text(size=5),
legend.spacing.y = unit(0.02, "cm"),
legend.margin=ggplot2::margin(0.1, 0.9, 0.1,-0.9, unit="cm"),
legend.box.margin=ggplot2::margin(0.1, 0.9, 0.1, -0.9, unit="cm"),
plot.margin = unit(c(-1.2, -1.2, -1.2, 0.1),"cm")
)
library(ggClusterNet)
barpath = paste(otupath,"/phy_tree_micro/",sep = "");print(barpath)
#> [1] ".//result_and_plot//Base_diversity_16s/OTU//phy_tree_micro/"
dir.create(barpath)
p6 = p1_1
p7 = p5_1
detach("package:ggstar")
FileName <- paste(barpath,Top_micro,"phy_tree_micro1", ".pdf", sep = "")
ggsave(FileName, p0, width = 6, height = 6)
FileName <- paste(barpath,Top_micro,"phy_tree_micro2", ".pdf", sep = "")
ggsave(FileName, p1, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro3", ".pdf", sep = "")
ggsave(FileName, p2, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro4", ".pdf", sep = "")
ggsave(FileName, p3, width = 12, height = 12)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".pdf", sep = "")
ggsave(FileName, p4, width = 15, height = 15)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".pdf", sep = "")
ggsave(FileName, p5, width = 18, height = 18)
FileName <- paste(barpath,Top_micro,"phy_tree_micro6", ".pdf", sep = "")
ggsave(FileName, p6, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro7", ".pdf", sep = "")
ggsave(FileName, p7, width = 12, height = 12)
# library(cowplot)
# save_plot(FileName, p2, base_height = 7, base_width =7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro1", ".png", sep = "")
ggsave(FileName, p0, width = 6, height = 6)
FileName <- paste(barpath,Top_micro,"phy_tree_micro2", ".png", sep = "")
ggsave(FileName, p1, width = 7, height = 7)
FileName <- paste(barpath,Top_micro,"phy_tree_micro3", ".png", sep = "")
ggsave(FileName, p2, width = 7, height = 7,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro4", ".png", sep = "")
ggsave(FileName, p3, width = 12, height = 12,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".png", sep = "")
ggsave(FileName, p4, width = 15, height = 15,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro5", ".png", sep = "")
ggsave(FileName, p5, width = 18, height = 18,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro6", ".png", sep = "")
ggsave(FileName, p6, width = 7, height = 7,dpi = 72)
FileName <- paste(barpath,Top_micro,"phy_tree_micro7", ".png", sep = "")
ggsave(FileName, p7, width = 12, height = 12,dpi = 72)Species composition-chord diagram（物种组成-和弦图）
Top = 10
rank = 7
path = barpath
ps_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) );ps_rela
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq() DNAStringSet: [ 37178 reference sequences ]
ps_P <- ps_rela %>%
ggClusterNet::tax_glom_wt( rank = rank)
ps_P
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 1074 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 1074 taxa by 7 taxonomic ranks ]
otu_P = as.data.frame((ggClusterNet::vegan_otu(ps_P)))
head(otu_P) tax_P = as.data.frame(ggClusterNet::vegan_tax(ps_P))
sub_design <- as.data.frame(phyloseq::sample_data(ps_P))
count2 = otu_P
#数据分组
iris.split <- split(count2,as.factor(sub_design$Group))
#数据分组计算平均值
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
ven2 = t(iris.combine)
# head(ven2)
lev = phyloseq::rank.names(ps)[rank]
Taxonomies <- ps %>%
ggClusterNet::tax_glom_wt(rank = rank) %>%
phyloseq::transform_sample_counts(function(x) {x/sum(x)} )%>%
phyloseq::psmelt() %>%
#filter(Abundance > 0.05) %>%
dplyr::arrange( !!sym(lev))
iris_groups<- dplyr::group_by(Taxonomies, !!sym(lev))
ps0_sum <- dplyr::summarise(iris_groups, mean(Abundance), sd(Abundance))
ps0_sum[is.na(ps0_sum)] <- 0
head(ps0_sum) colnames(ps0_sum) = c("ID","mean","sd")
ps0_sum <- dplyr::arrange(ps0_sum,desc(mean))
ps0_sum$mean <- ps0_sum$mean *100
ps0_sum <- as.data.frame(ps0_sum)
head(ps0_sum) top_P = ps0_sum$ID[1:Top];top_P
#> [1] "Unassigned" "Gemmatimonas_aurantiaca"
#> [3] "Gaiella_occulta" "Terrimonas_lutea"
#> [5] "Aquicella_siphonis" "Ralstonia_pickettii"
#> [7] "Bacillus_funiculus" "Pseudolabrys_taiwanensis"
#> [9] "Chryseolinea_serpens" "Pseudoduganella_violaceinigra"
### 开始进一步合并过滤
otu_P = as.data.frame(t(otu_P))
otu_tax = merge(ven2,tax_P,by = "row.names",all = F)
dim(otu_tax)
#> [1] 1074 11
otu_tax[,lev] = as.character(otu_tax[,lev])
otu_tax[,lev][is.na(otu_tax[,lev])] = "others"
i = 1
for (i in 1:nrow(otu_tax)) {
if(otu_tax[,lev] [i] %in% top_P){otu_tax[,lev] [i] = otu_tax[,lev] [i]}
else if(!otu_tax[,lev] [i] %in% top_P){otu_tax[,lev] [i] = "others"}
}
otu_tax[,lev] = as.factor(otu_tax[,lev])
head(otu_tax)
otu_mean = otu_tax[as.character(unique(sub_design$Group))]
head(otu_mean) row.names(otu_mean) = row.names(otu_tax)
iris.split <- split(otu_mean,as.factor(otu_tax[,lev]))
#数据分组计算平均值
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
mer_otu_mean = t(iris.combine)
head(mer_otu_mean )
#> Aquicella_siphonis Bacillus_funiculus Chryseolinea_serpens
#> Group1 0.04289304 0.02092137 0.02726656
#> Group2 0.03112277 0.06143216 0.02757814
#> Group3 0.03513122 0.01170578 0.02365613
#> Gaiella_occulta Gemmatimonas_aurantiaca others
#> Group1 0.07121335 0.1801137 0.6736228
#> Group2 0.11836097 0.1482847 0.9360435
#> Group3 0.06280437 0.1374640 0.7640420
#> Pseudoduganella_violaceinigra Pseudolabrys_taiwanensis
#> Group1 0.01523503 0.03331843
#> Group2 0.02901883 0.02958219
#> Group3 0.01784443 0.02983005
#> Ralstonia_pickettii Terrimonas_lutea Unassigned
#> Group1 0.05437782 0.03120037 4.849838
#> Group2 0.03491258 0.03582652 4.547838
#> Group3 0.01315802 0.05091932 4.853445
mi_sam = RColorBrewer::brewer.pal(9,"Set1")
mi_tax = colorRampPalette(RColorBrewer::brewer.pal(9,"Set3"))(length(row.names(mer_otu_mean)))
grid.col = NULL
#这里设置样品颜色
grid.col[as.character(unique(sub_design$Group))] = mi_sam
#设置群落中物种水平颜色
# grid.col[colnames(mer_otu_mean)] = mi_tax
grid.col[row.names(mer_otu_mean)] = mi_tax
FileName2 <- paste(path,"/",phyloseq::rank.names(ps)[rank],"_cricle",".pdf", sep = "")
pdf(FileName2, width = 12, height = 8)
#gap.degree修改间隔，不同小块之间的间隔
circlize::circos.par(gap.degree = c(rep(2, nrow(mer_otu_mean)-1), 10, rep(2, ncol(mer_otu_mean)-1), 10),
start.degree = 180)
circlize::chordDiagram(mer_otu_mean,
directional = F,
diffHeight = 0.06,
grid.col = grid.col,
reduce = 0,
transparency = 0.5,
annotationTrack =c("grid", "axis"),
preAllocateTracks = 2
)
circlize::circos.track(track.index = 1, panel.fun = function(x, y) {
circlize::circos.text(circlize::CELL_META$xcenter, circlize::CELL_META$ylim[1], circlize::CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))}, bg.border = NA)# here set bg.border to NA is important
circlize::circos.clear()
dev.off()
#> png
#> 2
grid.col = NULL
#这里设置样品颜色
grid.col[as.character(unique(sub_design$Group))] = mi_sam
#设置群落中物种水平颜色
# grid.col[colnames(mer_otu_mean)] = mi_tax
grid.col[row.names(mer_otu_mean)] = mi_tax
FileName2 <- paste(path,"/",phyloseq::rank.names(ps)[rank],"_cricle",".png", sep = "")
png(FileName2, res=150, width = 1000, height = 1008)
#gap.degree修改间隔，不同小块之间的间隔
circlize::circos.par(gap.degree = c(rep(2, nrow(mer_otu_mean)-1), 10, rep(2, ncol(mer_otu_mean)-1), 10),
start.degree = 180)
circlize::chordDiagram(mer_otu_mean,directional = F,
reduce = 0,
diffHeight = 0.06,grid.col = grid.col, transparency = 0.5, annotationTrack =c("grid", "axis"),
preAllocateTracks = 2
)
circlize::circos.track(track.index = 1, panel.fun = function(x, y) {
circlize::circos.text(circlize::CELL_META$xcenter, circlize::CELL_META$ylim[1], circlize::CELL_META$sector.index,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))}, bg.border = NA) # here set bg.border to NA is important
circlize::circos.clear()
dev.off()
#> png
#> 2Species composition-stack bar diagram（物种组成-堆叠柱状图）
library(ggalluvial)
group = "Group"
j = "Phylum"
label = TRUE
sd = FALSE
Top = 10
tran = TRUE
# axis_order = NA
# psdata = ps %>%
# tax_glom(taxrank = j)
psdata <- ggClusterNet::tax_glom_wt(ps = ps,ranks = j)
# transform to relative abundance
if (tran == TRUE) {
psdata = psdata%>%
phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
}
otu = phyloseq::otu_table(psdata)
tax = phyloseq::tax_table(psdata)
for (i in 1:dim(tax)[1]) {
if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
tax[i,j] =tax[i,j]
} else {
tax[i,j]= "others"
}
}
phyloseq::tax_table(psdata)= tax
Taxonomies <- psdata %>% # Transform to rel. abundance
phyloseq::psmelt()
Taxonomies$Abundance = Taxonomies$Abundance * 100
# Taxonomies$Abundance = Taxonomies$Abundance /rep
colnames(Taxonomies) <- gsub(j,"aa",colnames(Taxonomies))
data = c()
i = 2
for (i in 1:length(unique(phyloseq::sample_data(ps)$Group))) {
a <- as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,1]
b = as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,2]
c <- Taxonomies %>%
dplyr::filter(Group == a)
c$Abundance <- c$Abundance/b
data = data.frame(Sample =c$Sample,Abundance = c$Abundance,aa =c$aa,Group = c$Group)
if (i == 1) {
table = data
}
if (i != 1) {
table = rbind(table,data)
}
}
Taxonomies = table
#按照分组求均值
by_cyl <- dplyr::group_by(Taxonomies, aa,Group)
zhnagxu2 = dplyr::summarise(by_cyl, sum(Abundance), sd(Abundance))
iris_groups<- dplyr::group_by(Taxonomies, aa)
cc<- dplyr::summarise(iris_groups, sum(Abundance))
head(cc) colnames(cc)= c("aa","allsum")
cc<- dplyr::arrange(cc, desc(allsum))
##使用这个属的因子对下面数据进行排序
head(zhnagxu2) colnames(zhnagxu2) <- c("aa","group","Abundance","sd")
zhnagxu2$aa = factor(zhnagxu2$aa,order = TRUE,levels = cc$aa)
zhnagxu3 = zhnagxu2
##制作标签坐标，标签位于顶端
# Taxonomies_x = ddply(zhnagxu3,"group", summarize, label_y = cumsum(Abundance))
# head(Taxonomies_x )
#标签位于中部
# Taxonomies_x1 = ddply(zhnagxu3,"group", transform, label_y = cumsum(Abundance) - 0.5*Abundance)
Taxonomies_x = plyr::ddply(zhnagxu3,"group", summarize,label_sd = cumsum(Abundance),label_y = cumsum(Abundance) - 0.5*Abundance)
head( Taxonomies_x )
# Taxonomies_x$label_y =
Taxonomies_x = cbind(as.data.frame(zhnagxu3),as.data.frame(Taxonomies_x)[,-1])
Taxonomies_x$label = Taxonomies_x$aa
# #使用循环将堆叠柱状图柱子比较窄的别写标签，仅仅宽柱子写上标签
# for(i in 1:nrow(Taxonomies_x)){
# if(Taxonomies_x[i,3] > 3){
# Taxonomies_x[i,5] = Taxonomies_x[i,5]
# }else{
# Taxonomies_x[i,5] = NA
# }
# }
Taxonomies_x$aa = factor(Taxonomies_x$aa,order = TRUE,levels = c(as.character(cc$aa)))
##普通柱状图
p4 <- ggplot(Taxonomies_x , aes(x = group, y = Abundance, fill = aa, order = aa)) +
geom_bar(stat = "identity",width = 0.5,color = "black") +
# scale_fill_manual(values = colors) +
theme(axis.title.x = element_blank()) +
theme(legend.text=element_text(size=6)) +
scale_y_continuous(name = "Relative abundance (%)") +
guides(fill = guide_legend(title = j)) +
labs(x="",y="Relative abundance (%)",
title= "")
# paste("Top ",Top," ",j,sep = "")
p4
p4 = p4 + scale_x_discrete(limits = axis_order)
if (sd == TRUE) {
p4 = p4 +
geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
}
if (label == TRUE) {
p4 = p4 +
geom_text(aes(y = label_y, label = label ))
}
map = as.data.frame(phyloseq::sample_data(ps))
if (length(unique(map$Group))>3){p4 = p4 + theme(axis.text.x=element_text(angle=45,vjust=1, hjust=1))}
#-------------冲击图
cs = Taxonomies_x $aa
lengthfactor <- cs %>%
levels() %>%
length()
cs4 <- cs %>%
as.factor() %>%
summary() %>%
as.data.frame()
cs4$id = row.names(cs4)
#对因子进行排序
df_arrange<- dplyr::arrange(cs4, id)
#对Taxonomies_x 对应的列进行排序
Taxonomies_x1<- dplyr::arrange(Taxonomies_x , aa)
head(Taxonomies_x1) #构建flow的映射列Taxonomies_x
Taxonomies_x1$ID = factor(rep(c(1:lengthfactor), cs4$.))
#colour = "black",size = 2,,aes(color = "black",size = 0.8)
head(Taxonomies_x1) Taxonomies_x1$Abundance
#> [1] 39.237397 40.722278 40.228699 15.599818 12.611327 16.144444 16.726732
#> [8] 13.155390 14.172527 6.017195 9.123618 7.533416 6.089171 6.210184
#> [15] 6.063844 3.576966 4.740252 3.342673 3.661095 3.682421 4.121777
#> [22] 3.012819 3.265553 3.139758 3.001894 2.471411 2.291067 1.632678
#> [29] 2.740635 1.464264 1.444234 1.276930 1.497529
p3 <- ggplot(Taxonomies_x1, aes(x = group, y = Abundance,fill = aa,alluvium = aa,stratum = ID)) +
ggalluvial::geom_flow(aes(fill = aa, colour = aa),
stat = "alluvium", lode.guidance = "rightleft",
color = "black",size = 0.2,width = 0.35,alpha = .2) +
geom_bar(width = 0.45,stat = "identity") +
labs(x="",y="Relative abundance (%)",
title= "") +
guides(fill = guide_legend(title = j),color = FALSE) +
scale_y_continuous(expand = c(0,0))
p3
# flower plot
p1 <- ggplot(Taxonomies_x1,
aes(x = group, alluvium = aa, y = Abundance)) +
ggalluvial::geom_flow(aes(fill = aa, colour = aa), width = 0) +
labs(x="",y="Relative abundance (%)",
title="") +
guides(fill = guide_legend(title = j),color = FALSE) +
scale_y_continuous(expand = c(0,0))
p1 = p1 + scale_x_discrete(limits = axis_order)
p3 = p3 + scale_x_discrete(limits = axis_order)
# p3
if (label == TRUE) {
p1 = p1 +
geom_text(aes(y = label_y, label = label ))
p3 = p3 +
geom_text(aes(y = label_y, label = label ))
}
if (sd == TRUE) {
p1 = p1 +
geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
p3 = p3 +
geom_errorbar(aes(ymin=label_sd-sd, ymax=label_sd +sd), width=.2)
}
if (length(unique(map$Group))>3){ p3=p3+theme(axis.text.x=element_text(angle=45,vjust=1, hjust=1))}
# return(list(p4,Taxonomies,p3,p1))
barpath = paste(otupath,"/Microbial_composition/",sep = "")
dir.create(barpath)
phyloseq::rank_names(ps)
#> [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus" "Species"
p4_1 <- p4 +
# scale_fill_brewer(palette = "Paired") +
scale_fill_manual(values = colset3) +
scale_x_discrete(limits = axis_order) +
mytheme1
p4_1
p4_2 <- p3 +
# scale_fill_brewer(palette = "Paired") +
scale_fill_manual(values = colset3) +
scale_x_discrete(limits = axis_order) +
mytheme1
p4_2
databar <- Taxonomies
FileName1 <- paste(barpath,"/a2_",j,"_barflow",".pdf", sep = "")
ggsave(FileName1, p4_2, width = (5+ gnum), height =8 )
FileName2 <- paste(barpath,"/a2_",j,"_barflow",".jpg", sep = "")
ggsave(FileName2, p4_2, width = (5+ gnum), height =8 )
FileName1 <- paste(barpath,"/a2_",j,"_bar",".pdf", sep = "")
ggsave(FileName1, p4_1, width = (5+ gnum), height =8 )
FileName2 <- paste(barpath,"/a2_",j,"_bar",".jpg", sep = "")
ggsave(FileName2, p4_1, width = (5+ gnum), height =8 )
FileName <- paste(barpath,"/a2_",j,"_bar_data",".csv", sep = "")
write.csv(databar,FileName)
detach("package:ggalluvial")Species composition-circle strack bar diagram（物种组成-环状堆叠柱状图）
# BiocManager::install("ggstance")
ps = readRDS("./data/dataNEW/ps_16s.rds")
library(ggtree)
barpath = paste(otupath,"/circle_Micro_strack_bar/",sep = "");print(barpath)
#> [1] ".//result_and_plot//Base_diversity_16s/OTU//circle_Micro_strack_bar/"
dir.create(barpath)
Top = 15
dist = "bray"
cuttree = 3
hcluter_method = "complete"
# phyloseq(ps)对象标准化
otu = ps %>%
ggClusterNet::scale_micro() %>%
ggClusterNet::vegan_otu() %>% t() %>%
as.data.frame()
# 导出OTU表
# otu = as.data.frame(t(vegan_otu(ps1_rela)))
#计算距离矩阵
unif = phyloseq::distance(ps %>% ggClusterNet::scale_micro() , method = dist)
# 聚类树，method默认为complete
hc <- stats::hclust(unif, method = hcluter_method )
# take grouping with hcluster tree
clus <- cutree(hc, cuttree )
d = data.frame(label = names(clus),
member = factor(clus))
# eatract mapping file
map = as.data.frame(phyloseq::sample_data(ps))
dd = merge(d,map,by = "row.names",all = F)
row.names(dd) = dd$Row.names
dd$Row.names = NULL
# library(tidyverse)
# ggtree绘图 #----
p = ggtree::ggtree(hc, layout='circular') %<+% dd +
geom_tippoint(size=5, shape=21, aes(fill= Group, x=x)) +
geom_tiplab(aes(color = Group,x=x * 1.2), hjust=1,offset=0.3) + xlim(-0.5,NA)
p
psdata = ggClusterNet::tax_glom_wt(ps = ps %>% ggClusterNet::scale_micro(),ranks = "Phylum" )
# 转化丰度值
psdata = psdata%>% phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
#--提取otu和物种注释表格
otu = phyloseq::otu_table(psdata)
tax = phyloseq::tax_table(psdata)
head(tax)
#> Taxonomy Table: [6 taxa by 2 taxonomic ranks]:
#> Kingdom Phylum
#> Acidobacteria "Bacteria" "Acidobacteria"
#> Actinobacteria "Bacteria" "Actinobacteria"
#> Aminicenantes "Bacteria" "Aminicenantes"
#> Armatimonadetes "Bacteria" "Armatimonadetes"
#> Bacteroidetes "Bacteria" "Bacteroidetes"
#> BRC1 "Bacteria" "BRC1"
#--按照指定的Top数量进行筛选与合并
j = "Phylum"
for (i in 1:dim(tax)[1]) {
if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
tax[i,j] =tax[i,j]
} else {
tax[i,j]= "Other"
}
}
phyloseq::tax_table(psdata)= tax
Taxonomies <- psdata %>% phyloseq::psmelt()
Taxonomies$Abundance = Taxonomies$Abundance * 100
Taxonomies$OTU = NULL
colnames(Taxonomies)[1] = "id"
head(Taxonomies)
dat2 = data.frame(id = Taxonomies$id,Abundance = Taxonomies$Abundance,Phylum = Taxonomies$Phylum)
head(dat2)
p2 <- p +
ggnewscale::new_scale_fill() +
ggtreeExtra::geom_fruit(
data=dat2,
geom=geom_bar,
mapping=aes(
x=Abundance,
y=id,
fill= Phylum
),
stat="identity",
width = 0.4,
orientation="y",
offset=0.9,
pwidth=2,
axis.params=list(
axis = "x",
text.angle = -45,
hjust = 0,
vjust = 0.5,
nbreak = 4
)
) +
scale_fill_manual(
# name = "Number of interactions",
values=c(colset3),
guide=guide_legend(keywidth=0.5,keyheight=0.5,order=5)
) +theme_void()
FileName2 <- paste(barpath,"/a2_","_bar",".jpg", sep = "")
ggsave(FileName2, p2, width = 10, height =8 )
FileName2 <- paste(barpath,"/a2_","_bar",".pdf", sep = "")
ggsave(FileName2, p2, width = 10, height =8 )
#--距离和丰度合并#----
dist = "bray"
j = "Phylum"# 使用门水平绘制丰度图表
# rep = 6 # 重复数量是6个
Top = 10 # 提取丰度前十的物种注释
tran = TRUE # 转化为相对丰度值
hcluter_method = "complete"
Group = "Group"
cuttree = 3
# phyloseq(ps)对象标准化
ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )
# 导出OTU表
otu = as.data.frame(t(ggClusterNet::vegan_otu(ps1_rela)))
#计算距离矩阵
unif = phyloseq::distance(ps1_rela , method = dist)
# 聚类树，method默认为complete
hc <- stats::hclust(unif, method = hcluter_method )
# take grouping with hcluster tree
clus <- stats::cutree(hc, cuttree )
# 提取树中分组的标签和分组编号
d = data.frame(label = names(clus),
member = factor(clus))
# eatract mapping file
map = as.data.frame(phyloseq::sample_data(ps))
# 合并树信息到样本元数据
dd = merge(d,map,by = "row.names",all = F)
row.names(dd) = dd$Row.names
dd$Row.names = NULL
# library(tidyverse)
# ggtree绘图 #----
p = ggtree::ggtree(hc) %<+% dd +
geom_tippoint(size=5, shape=21, aes(fill= Group, x=x)) +
geom_tiplab(aes(color = Group,x=x * 1.2), hjust=1)
p
# 按照分类学门(j)合并
psdata = ggClusterNet::tax_glom_wt(ps = ps1_rela,ranks = j)
# 转化丰度值
if (tran == TRUE) {
psdata = psdata %>% phyloseq::transform_sample_counts(function(x) {x/sum(x)} )
}
#--提取otu和物种注释表格
otu = phyloseq::otu_table(psdata)
tax = phyloseq::tax_table(psdata)
#--按照指定的Top数量进行筛选与合并
for (i in 1:dim(tax)[1]) {
if (row.names(tax)[i] %in% names(sort(rowSums(otu), decreasing = TRUE)[1:Top])) {
tax[i,j] =tax[i,j]
} else {
tax[i,j]= "Other"
}
}
phyloseq::tax_table(psdata)= tax
##转化为表格
Taxonomies <- psdata %>%
phyloseq::psmelt()
head(Taxonomies) Taxonomies$Abundance = Taxonomies$Abundance * 100
Taxonomies$OTU = NULL
colnames(Taxonomies)[1] = "id"
head(Taxonomies)
p <- p + ggnewscale::new_scale_fill()
p
p1 <- facet_plot(p, panel = 'Stacked Barplot', data = Taxonomies, geom = ggstance::geom_barh,mapping = aes(x = Abundance, fill = !!sym(j)),color = "black",stat='identity' )
p1
grotax <- Taxonomies %>%
dplyr::group_by(Group,!!sym(j)) %>%
dplyr::summarise(Abundance = sum(Abundance))
head(grotax)
data = c()
i = 2
for (i in 1:length(unique(phyloseq::sample_data(psdata)$Group))) {
a <- as.data.frame(table(phyloseq::sample_data(psdata)$Group))[i,1]
b = as.data.frame(table(phyloseq::sample_data(psdata)$Group))[i,2]
c <- grotax %>%
dplyr::filter(Group == a)
c$Abundance <- c$Abundance/b
head(c)
# data = data.frame(Sample =c$Sample,Abundance = c$Abundance,aa =c$aa,Group = c$Group)
data = c
if (i == 1) {
table = data
}
if (i != 1) {
table = rbind(table,data)
}
}
sum( grotax$Abundance)
#> [1] 1800
head(table)
#--绘制分组的聚类结果
ps1_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) )
#按照分组合并OTU表格
otu = as.data.frame((ggClusterNet::vegan_otu(ps1_rela)))
iris.split <- split(otu,as.factor(phyloseq::sample_data(ps1_rela)$Group))
iris.apply <- lapply(iris.split,function(x)colMeans(x))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
otuG = t(iris.combine)
ps = phyloseq::phyloseq(phyloseq::otu_table(otuG,taxa_are_rows = T),
phyloseq::tax_table(ps1_rela)
)
hc = ps %>%
phyloseq::distance(method = dist) %>%
stats::hclust( method = hcluter_method )
# take grouping with hcluster tree
clus <- cutree(hc, cuttree )
# 提取树中分组的标签和分组编号
d = data.frame(label = names(clus),
member = factor(clus))
# eatract mapping file
map = data.frame(ID = unique(phyloseq::sample_data(ps1_rela)$Group),row.names = unique(phyloseq::sample_data(ps1_rela)$Group),Group = unique(phyloseq::sample_data(ps1_rela)$Group))
# 合并树信息到样本元数据
dd = merge(d,map,by = "row.names",all = F)
row.names(dd) = dd$Row.names
dd$Row.names = NULL
# ggtree绘图 #----
p3 = ggtree(hc) %<+% dd +
geom_tippoint(size=5, shape=21, aes(fill= member, x=x)) +
geom_tiplab(aes(color = member,x=x * 1.2), hjust=1)
p3
p3 <- p3 + ggnewscale::new_scale_fill()
head(grotax)
p4 <- facet_plot(p3, panel = 'Stacked Barplot', data = table, geom = ggstance::geom_barh,mapping = aes(x = Abundance, fill = !!sym(j)),color = "black",stat='identity' )
p4
# return(list(p,p1,p3,p4,Taxonomies))
p5_1 <- p
p5_2 <- p1
p5_3 <- p3
p5_4 <- p4
clubardata <- Taxonomies
ps = readRDS("./data/dataNEW/ps_16s.rds")
FileName1 <- paste(barpath,"/a2_",j,"_cluster_sample",".pdf", sep = "")
ggsave(FileName1, p5_1, width = 6, height = dim(phyloseq::sample_data(ps))[1]/4,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_sample",".pdf", sep = "")
ggsave(FileName1, p5_2, width = 12, height = dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_sample",".jpg", sep = "")
ggsave(FileName1, p5_1, width = 6, height =dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_sample",".jpg", sep = "")
ggsave(FileName1, p5_2, width = 12, height =dim(phyloseq::sample_data(ps))[1]/4 ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_Group",".pdf", sep = "")
ggsave(FileName1, p5_3, width = 6, height = gnum,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_Group",".pdf", sep = "")
ggsave(FileName1, p5_4, width = 12, height = gnum ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_Group",".jpg", sep = "")
ggsave(FileName1, p5_3, width = 6, height = gnum ,limitsize = FALSE)
FileName1 <- paste(barpath,"/a2_",j,"_cluster_bar_Group",".jpg", sep = "")
ggsave(FileName1, p5_4, width = 12, height = gnum ,limitsize = FALSE)
FileName <- paste(barpath,"/a2_",j,"_cluster_bar_data",".csv", sep = "")
write.csv(clubardata,FileName)Ternary diagram（三元图）
library(ggtern)
ternpath = paste(otupath,"/ggtern/",sep = "")
dir.create(ternpath)
ps = readRDS("./data/dataNEW/ps_16s.rds")
ternpath = ternpath
ps_rela = phyloseq::transform_sample_counts(ps, function(x) x / sum(x) );ps_rela
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 37178 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq() DNAStringSet: [ 37178 reference sequences ]
otu = ggClusterNet::vegan_otu(ps_rela) %>% as.data.frame()
#数据分组
iris.split <- split(otu,as.factor(as.factor(phyloseq::sample_data(ps)$Group)))
#数据分组计算平均值
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
ven2 = t(iris.combine) %>% as.data.frame()
head(ven2) A <- combn(colnames(ven2),3)
ven2$mean = rowMeans(ven2)
tax = ggClusterNet::vegan_tax(ps)
otutax = cbind(ven2,tax)
head(otutax) otutax$Phylum[otutax$Phylum == ""] = "Unknown"
# i = 1
for (i in 1:dim(A)[2]) {
x = A[1,i]
y = A[2,i]
z = A[3,i]
p <- ggtern::ggtern(data=otutax,aes_string(x = x,y=y,z=z,color = "Phylum",size ="mean" ))+geom_point() + theme_void()
p
filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.pdf",sep = "")
ggsave(filename,p,width = 12,height = 10)
filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.png",sep = "")
ggsave(filename,p,width = 12,height = 10,dpi = 100)
filename = paste(ternpath,"/",paste(x,y,z,sep = "_"),"_OTU.csv",sep = "")
write.csv(otutax,filename,quote = FALSE)
}
detach("package:ggtern")Total microbe + Specific microbe (共有微生物+特有微生物)
flowpath = paste(otupath,"/flowplot/",sep = "")
dir.create(flowpath)
group="Group"
#rep=6
m1=2
start=1
a=0.2
b=1
lab.leaf=1
col.cir="yellow"
a.cir=0.5
b.cir=0.5
m1.cir=2
N=0.5
mapping = as.data.frame(phyloseq::sample_data(ps))
aa = ggClusterNet::vegan_otu(ps)
otu_table = as.data.frame(t(aa))
count = aa
sub_design <- as.data.frame(phyloseq::sample_data(ps))
sub_design$SampleType = sub_design$Group
phyloseq::sample_data(ps ) = sub_design
count[count > 0] <- 1
count2 = as.data.frame(count)
# group
iris.split <- split(count2,as.factor(sub_design$Group))
#group mean
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# conbine result
iris.combine <- do.call(rbind,iris.apply)
ven2 = t(iris.combine)
for (i in 1:length(unique(phyloseq::sample_data(ps)$Group))) {
aa <- as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,1]
bb = as.data.frame(table(phyloseq::sample_data(ps)$Group))[i,2]
ven2[,aa] = ven2[,aa]/bb
}
ven2[ven2 < N] = 0
ven2[ven2 >=N] = 1
ven2 = as.data.frame(ven2)
ven3 = as.list(ven2)
ven2 = as.data.frame(ven2)
all_num = dim(ven2[rowSums(ven2) == length(levels(sub_design$Group)),])[1]
ven2[,1] == 1
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#> [7153] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [7213] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [7225] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
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#> [21793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [21817] FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
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#> [21841] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [21853] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [21865] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
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#> [21889] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
#> [21901] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE
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#> [21925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [21961] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
#> [21973] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [21985] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE TRUE
#> [21997] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
#> [22009] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
#> [22021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
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#> [24901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
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#> [24973] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [25069] TRUE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25081] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
#> [25093] FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25105] TRUE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [25117] FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
#> [25129] TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> [25141] FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE
#> [25153] TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
#> [25165] TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
#> [25177] TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
#> [25189] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
#> [25201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [25213] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
#> [25225] FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [25237] FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
#> [25249] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
#> [25261] FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
#> [25273] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25285] TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
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#> [25309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [25321] TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE FALSE
#> [25333] TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
#> [25345] FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [25357] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
#> [25369] TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25381] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25393] TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
#> [25405] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [25429] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
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#> [25465] FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [25489] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25501] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
#> [25513] FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
#> [25525] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [25537] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
#> [25549] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [25561] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE
#> [25573] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
#> [25585] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
#> [25597] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [25609] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE
#> [25621] TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
#> [25633] TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25645] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [25657] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
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#> [25693] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [25741] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25753] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [25801] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [25825] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [25849] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25861] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25873] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25885] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
#> [25897] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25909] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [25921] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [25933] FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25945] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [25957] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [25981] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
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#> [26005] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26017] FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [26029] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
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#> [26053] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26065] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26077] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
#> [26089] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [26101] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [26125] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [26137] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [26185] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26197] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
#> [26209] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26221] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26233] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
#> [26245] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [26257] TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [26269] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [26281] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26293] FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
#> [26305] TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
#> [26317] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> [26329] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
#> [26341] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
#> [26353] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE
#> [26365] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26377] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [26389] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [26401] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE
#> [26413] TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
#> [26425] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26437] TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [26449] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26461] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
#> [26473] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [26485] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
#> [26497] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26509] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [26521] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [26533] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26545] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26557] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [26569] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE
#> [26581] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
#> [26593] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
#> [26605] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
#> [26617] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE
#> [26629] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
#> [26641] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
#> [26653] TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [26665] TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
#> [26677] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
#> [26689] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE
#> [26701] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
#> [26713] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26725] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [26737] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
#> [26749] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
#> [26761] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
#> [26773] FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE FALSE
#> [26785] FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE
#> [26797] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE
#> [26809] FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [26821] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
#> [26833] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [26845] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [26857] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [26869] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
#> [26881] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [26893] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE
#> [26905] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE
#> [26917] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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#> [30925] FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
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#> [31009] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
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#> [31105] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
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#> [31129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
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#> [31153] FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
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#> [31201] FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [31285] FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
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#> [31309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE
#> [31321] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
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#> [31357] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
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#> [31465] FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
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#> [31489] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
#> [31501] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [31561] TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
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#> [31657] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
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#> [31681] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31693] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE FALSE FALSE
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#> [31717] FALSE FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
#> [31729] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [31765] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [31777] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [31789] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
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#> [36541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
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#> [36577] FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
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#> [36625] FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
#> [36637] FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE
#> [36649] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
#> [36661] TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
#> [36673] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
#> [36685] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
#> [36709] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE
#> [36721] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE
#> [36733] FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [36745] FALSE TRUE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
#> [36757] TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
#> [36769] FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE
#> [36781] TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [36793] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
#> [36805] TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
#> [36817] FALSE FALSE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE
#> [36829] TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE FALSE FALSE
#> [36841] FALSE TRUE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
#> [36853] TRUE FALSE TRUE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE
#> [36865] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE
#> [36877] TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE
#> [36889] FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
#> [36901] TRUE FALSE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE
#> [36913] FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE
#> [36925] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [36937] TRUE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
#> [36949] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [36961] FALSE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [36973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
#> [36985] FALSE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE
#> [36997] TRUE TRUE FALSE TRUE TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE
#> [37009] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE
#> [37021] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#> [37033] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
#> [37045] TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
#> [37057] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
#> [37069] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
#> [37081] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
#> [37093] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE TRUE
#> [37105] TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE FALSE
#> [37117] FALSE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE TRUE
#> [37129] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE TRUE
#> [37141] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37153] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
#> [37165] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [37177] FALSE FALSE
A = rep("A",length(colnames(ven2)))
B = rep(1,length(colnames(ven2)))
i = 1
for (i in 1:length(colnames(ven2))) {
B[i] = length(ven2[rowSums(ven2) == 1,][,i][ven2[rowSums(ven2) == 1,][,i] == 1])
A[i] = colnames(ven2)[i]
}
n <- length(A)
deg <- 360 / n
t = 1:n
print(deg)
#> [1] 120
p <- ggplot() +
# geom_point(aes(x = 5 + cos((start + deg * (t - 1)) * pi / 180) * lab.leaf, y = 5 + sin((start + deg * (t - 1)) * pi / 180) *lab.leaf)) +
ggforce::geom_ellipse(aes(x0 = 5 + cos((start + deg * (t - 1)) * pi / 180),
y0 = 5 + sin((start + deg * (t - 1)) * pi / 180),
a = a,
b = b,
angle = (n/2 +seq(0,1000,2)[1:n])/n * pi,
m1 = m1,
fill = as.factor(1:n)),show.legend = F) +
ggforce::geom_ellipse(aes(x0 = 5,y0 = 5,a = a.cir,b = b.cir,angle = 0,m1 = m1.cir),fill = col.cir) +
geom_text(aes(x = 5,y = 5,label = paste("OVER :",all_num,sep = ""))) +
geom_text(aes(
x = 5 + cos((start + deg * (t - 1)) * pi / 180) * lab.leaf,
y = 5 + sin((start + deg * (t - 1)) * pi / 180) * lab.leaf,
label = paste(A,":",B,sep = "")),angle = 360/n*((1:n)-1) ) +
coord_fixed() + theme_void()
p0_2 = p
FileName1 <- paste(flowpath,"ggflowerGroup.pdf", sep = "")
ggsave(FileName1, p0_2, width = 14, height = 14)
FileName2 <- paste(flowpath,"ggflowerGroup.jpg", sep = "")
ggsave(FileName2, p0_2, width = 14, height = 14 )Venn diagram（维恩图）
Venpath = paste(otupath,"/Ven_Upset_super/",sep = "")
dir.create(Venpath)
group = "Group"
path = path
N = 0.5
aa = ggClusterNet::vegan_otu(ps)
otu_table = as.data.frame(t(aa))
count = aa
countA = count
sub_design <- as.data.frame(phyloseq::sample_data(ps))
#
# pick_val_num <- rep/2
count[count > 0] <- 1
count2 = as.data.frame(count )
aa = sub_design[,"Group"]
colnames(aa) = "Vengroup"
#-div group
iris.split <- split(count2,as.factor(aa$Vengroup))
#数据分组计算平均值
iris.apply <- lapply(iris.split,function(x)colSums(x[]))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
ven2 = t(iris.combine)
for (i in 1:length(unique(phyloseq::sample_data(ps)[,"Group"]))) {
aa <- as.data.frame(table(phyloseq::sample_data(ps)[,"Group"]))[i,1]
bb = as.data.frame(table(phyloseq::sample_data(ps)[,"Group"]))[i,2]
ven2[,aa] = ven2[,aa]/bb
}
ven2[ven2 < N] = 0
ven2[ven2 >=N] = 1
ven2 = as.data.frame(ven2)
#
ven3 = as.list(ven2)
# ven_pick = get.venn.partitions(ven3)
for (i in 1:ncol(ven2)) {
ven3[[i]] <- row.names(ven2[ven2[i] == 1,])
}
if (length(names(ven3)) == 2) {
filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
pdf(file=filename3,width = 12, height = 8)
a<- VennDiagram::venn.diagram(ven3,
filename=NULL,
lwd=2,#圈线粗度
lty=1, #圈线类型
fill=c('red',"blue"), #填充颜色
col=c('red',"blue"), #圈线颜色
cat.col=c('red',"blue"),#A和B的颜色
cat.cex = 4,# A和B的大小
rotation.degree = 0,#旋转角度
main = "",#主标题内容
main.cex = 2,#主标题大小
sub = "",#亚标题内容
sub.cex = 1,#亚标题字大小
cex=3,#里面交集字的大小
alpha = 0.5,#透明度
reverse=TRUE,
scaled = FALSE)
grid.draw(a)
dev.off()
filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
jpeg(file=filename33)
grid.draw(a)
dev.off();
} else if (length(names(ven3)) == 3) {
filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
pdf(file=filename3,width = 18, height = 15)
a<- VennDiagram::venn.diagram(ven3,
filename=NULL,
lwd=2,#圈线粗度
lty=1, #圈线类型
fill=c('red',"blue","yellow"), #填充颜色
col=c('red',"blue","yellow"), #圈线颜色
cat.col=c('red',"blue","yellow"),#A和B的颜色
cat.cex = 4,# A和B的大小
rotation.degree = 0,#旋转角度
main = "",#主标题内容
main.cex = 2,#主标题大小
sub = "",#亚标题内容
sub.cex = 1,#亚标题字大小
cex=3,#里面交集字的大小
alpha = 0.5,#透明度
reverse=TRUE,
scaled = FALSE)
grid::grid.draw(a)
dev.off()
filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
jpeg(file=filename33)
grid::grid.draw(a)
dev.off()
grid::grid.draw(a)
} else if (length(names(ven3)) == 4) {
filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
pdf(file=filename3,width = 18, height = 15)
a<-VennDiagram::venn.diagram(ven3,
filename=NULL,
lwd=2,#圈线粗度
lty=1, #圈线类型
fill=c('red',"blue","yellow","#7ad2f6"), #填充颜色
col=c('red',"blue","yellow","#7ad2f6"), #圈线颜色
cat.col=c('red',"blue","yellow","#7ad2f6"),#A和B的颜色
cat.cex = 4,# A和B的大小
rotation.degree = 0,#旋转角度
main = "",#主标题内容
main.cex = 2,#主标题大小
sub = "",#亚标题内容
sub.cex = 1,#亚标题字大小
cex=3,#里面交集字的大小
alpha = 0.5,#透明度
reverse=TRUE,
scaled = FALSE)
grid::grid.draw(a)
dev.off()
filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
jpeg(file=filename33)
grid::grid.draw(a)
dev.off()
grid::grid.draw(a)
}else if (length(names(ven3)) == 5) {
filename3 = paste(path,"ven_",paste(names(ven3),sep = "",collapse="-"),".pdf",sep = "",collapse="_")
pdf(file=filename3,width = 12, height = 12)
a<- VennDiagram::venn.diagram(ven3,
filename=NULL,
lwd=2,#圈线粗度
lty=1, #圈线类型
fill=c('red',"blue","yellow","#7ad2f6","green"), #填充颜色
col=c('red',"blue","yellow","#7ad2f6","green"), #圈线颜色
cat.col=c('red',"blue","yellow","#7ad2f6","green"),#A和B的颜色
cat.cex = 4,# A和B的大小
rotation.degree = 0,#旋转角度
main = "",#主标题内容
main.cex = 2,#主标题大小
sub = "",#亚标题内容
sub.cex = 1,#亚标题字大小
cex=3,#里面交集字的大小
alpha = 0.5,#透明度
reverse=TRUE,
scaled = FALSE)
grid::grid.draw(a)
dev.off()
filename33 = paste(path,"ven",".jpg",sep = "",collapse="_")
jpeg(file=filename33)
grid::grid.draw(a)
dev.off()
grid::grid.draw(a)
}else if (length(names(ven3)) == 6) {
print("ven not use for more than 6")
}
Venn-network diagram（维恩网络图）
library(ggClusterNet)
library(phyloseq)
biospath = paste(otupath,"/biospr_network_Ven/",sep = "")
dir.create(biospath)
N = 0.5
result = ggClusterNet::div_network(ps)
edge = result[[1]]
data = result[[3]]
result <- ggClusterNet::div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)
# result <- div_culculate(table = result[[3]],distance = 1,distance2 = 1.2,distance3 = 1.1,order = FALSE)
edge = result[[1]]
plotdata = result[[2]]
#--这部分数据是样本点数据
groupdata <- result[[3]]
# table(plotdata$elements)
node = plotdata[plotdata$elements == unique(plotdata$elements), ]
otu_table = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
tax_table = as.data.frame(ggClusterNet::vegan_tax(ps))
res = merge(node,tax_table,by = "row.names",all = F)
row.names(res) = res$Row.names
res$Row.names = NULL
plotcord = res
xx = data.frame(mean =rowMeans(otu_table))
plotcord = merge(plotcord,xx,by = "row.names",all = FALSE)
head(plotcord)# plotcord$Phylum
row.names(plotcord) = plotcord$Row.names
plotcord$Row.names = NULL
head(plotcord)library(ggrepel)
head(plotcord)head(groupdata)p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),
data = edge, size = 0.3,color = "yellow") +
geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +
geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +
geom_text(aes(X1, X2,label = elements ), data = groupdata,hjust = 1,vjust = -1) +
theme_void()
p
filename = paste(biospath,"/","biostr_Ven_network.pdf",sep = "")
ggsave(filename,p,width = (15),height = (12))
filename = paste(biospath,"/","biostr_Ven_network.jpg",sep = "")
ggsave(filename,p,width = (15),height = (12))
detach("package:ggClusterNet")
detach("package:phyloseq")Sankey diagram（桑基图）
# BiocManager::install("networkD3")
# BiocManager::install("webshot")
library(ggClusterNet)
library(phyloseq)
snapath = paste(otupath,"/sankeyNetwork/",sep = "");otupath
#> [1] ".//result_and_plot//Base_diversity_16s/OTU/"
dir.create(snapath)
ps = readRDS("./data/dataNEW/ps_16s.rds")
rank=6#参数目前不可修改
Top=50
snapath=snapath
map = sample_data(ps) %>% as.tibble()
otu = ps %>% vegan_otu() %>%
as.data.frame()
otu$ID = row.names(otu)
tax = ps %>%
scale_micro() %>%
vegan_tax() %>%
as.data.frame()
tax$taxid = row.names(tax)
head(tax)
data = map %>% as.tibble() %>%
inner_join(otu) %>%
gather( taxa, count, starts_with("ASV_")) %>%
inner_join(tax,by = c("taxa" = "taxid") )
data
tax = ps %>%
ggClusterNet::tax_glom_wt(ranks = rank) %>%
ggClusterNet::filter_OTU_ps(Top) %>%
subset_taxa(!Genus %in% c("Unassigned","Unknown")) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
head(tax) dim(tax)
#> [1] 49 6
id2 = c("k","p","c","o","f","g")
dat = NULL
for (i in 1:5) {
dat <- tax[,c(i,i+1)] %>% distinct(.keep_all = TRUE)
colnames(dat) = c("source","target")
dat$source = paste(id2[i],dat$source,sep = "_")
dat$target = paste(id2[i+1],dat$target,sep = "_")
if (i == 1) {
dat2 = dat
}
dat2 = rbind(dat2,dat)
}
dim(dat2)
#> [1] 169 2
# dat2 = dat2 %>% distinct(.keep_all = TRUE)
head(dat2)
otu = ps %>%
ggClusterNet::tax_glom_wt(ranks = 6) %>%
ggClusterNet::scale_micro() %>%
ggClusterNet::filter_OTU_ps(Top) %>%
subset_taxa(!Genus %in% c("Unassigned","Unknown")) %>%
ggClusterNet::vegan_otu() %>%
t() %>%
as.data.frame()
head(otu) otutax = cbind(otu,tax)
id = rank.names(ps)[1:6]
dat = NULL
dat3 = NULL
head(data)
for (j in 1) {
dat = data %>% group_by(Group,!!sym(rank_names(ps)[j])) %>%
summarise_if(is.numeric,sum,na.rm = TRUE)
colnames(dat) = c("source","target","value")
dat$target = paste(id2[j],dat$target,sep = "_")
if (j == 1) {
dat3 = dat
}
# dat3 = rbind(dat3,dat)
}
dat3 %>% tail() tem = data.frame(target = dat3$target,value = dat3$value)
dat3$value = NULL
dat2 = rbind(dat2,dat3)
head( otutax) for (i in 1:6) {
dat <- otutax %>%
dplyr::group_by(!!sym(id[i])) %>%
summarise_if(is.numeric,sum,na.rm = TRUE)
dat = data.frame(Genus = dat[,1],value =rowSums(dat[,-1]) )
colnames(dat) = c("target","value")
dat$target = paste(id2[i],dat$target,sep = "_")
if (i == 1) {
dat3 = dat
}
dat3 = rbind(dat3,dat)
}
dat4 <- dat2 %>% left_join(dat3)
sankey = dat4
head( sankey )
nodes <- data.frame(name = unique(c(as.character(sankey$source),as.character(sankey$target))),stringsAsFactors = FALSE)
nodes$ID <- 0:(nrow(nodes)-1)
sankey <- merge(sankey,nodes,by.x = "source",by.y = "name")
sankey <- merge(sankey,nodes,by.x = "target",by.y = "name")
colnames(sankey) <- c("X","Y","value","source","target")
sankey <- subset(sankey,select = c("source","target","value"))
nodes <- subset(nodes,select = c("name"))
ColourScal='d3.scaleOrdinal() .range(["#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"])'
sankey$energy_type <- sub(' .*', '', nodes[sankey$source + 1, 'name'])
library(networkD3)
p <- sankeyNetwork(Links = sankey, Nodes = nodes,
Source = "source",Target = "target",Value = "value",
NodeID = "name",
sinksRight=FALSE,
LinkGroup = 'energy_type',
colourScale= ColourScal,
# nodeWidth=40,
# fontSize=13
# nodePadding=20
)
# return(list(p,sankey))
dat = sankey
FileName <-paste(snapath,"/sankey_Group.csv", sep = "")
write.csv(dat,FileName,sep = "")
saveNetwork(p,paste(snapath,"/sankey_Group.html", sep = ""))
library(webshot)
# webshot::install_phantomjs()
# webshot(paste(snapath,"/sankey1.html", sep = "") ,paste(snapath,"/sankey1.png", sep = ""))
webshot(paste(snapath,"/sankey_Group.html", sep = "") , paste(snapath,"/sankey_Group.pdf", sep = ""))2.Difference analysis (2.差异分析)
edgeR and DESep2-Volcano plot（edgeR和DESep2-火山图）
ps = readRDS("./data/dataNEW/ps_16s.rds")
diffpath = paste(otupath,"/diff_tax/",sep = "")
dir.create(diffpath)
diffpath.1 = paste(diffpath,"/DEsep2/",sep = "")
dir.create(diffpath.1)
diffpath.2 = diffpath.1
# 准备脚本
group = "Group"
pvalue = 0.05
lfc =0
artGroup = NULL
method = "TMM"
j = 2
path = diffpath
b = NULL
if (j %in% c("OTU","gene","meta")) {
ps = ps
} else if (j %in% c(1:7)) {
ps = ps %>%
ggClusterNet::tax_glom_wt(ranks = j)
} else if (j %in% c("Kingdom","Phylum","Class","Order","Family","Genus","Species")){
} else {
ps = ps
print("unknown j, checked please")
}
sub_design <- as.data.frame(phyloseq::sample_data(ps))
Desep_group <- as.character(levels(as.factor(sub_design$Group)))
Desep_group
#> [1] "Group1" "Group2" "Group3"
if ( is.null(artGroup)) {
#--构造两两组合#-----
aaa = combn(Desep_group,2)
# sub_design <- as.data.frame(sample_data(ps))
}
if (!is.null(artGroup)) {
aaa = as.matrix(b)
}
otu_table = as.data.frame(ggClusterNet::vegan_otu(ps))
count = as.matrix(otu_table)
count <- t(count)
sub_design <- as.data.frame(phyloseq::sample_data(ps))
dim(sub_design)
#> [1] 18 2
sub_design$SampleType = as.character(sub_design$Group)
sub_design$SampleType <- as.factor(sub_design$Group)
# create DGE list
d = edgeR::DGEList(counts=count, group=sub_design$SampleType)
d$samples d = edgeR::calcNormFactors(d,method=method)#默认为TMM标准化
# Building experiment matrix
design.mat = model.matrix(~ 0 + d$samples$group)
colnames(design.mat)=levels(sub_design$SampleType)
d2 = edgeR::estimateGLMCommonDisp(d, design.mat)
d2 = edgeR::estimateGLMTagwiseDisp(d2, design.mat)
fit = edgeR::glmFit(d2, design.mat)
#------------根据分组提取需要的差异结果#------------
for (i in 1:dim(aaa)[2]) {
# i = 1
Desep_group = aaa[,i]
print( Desep_group)
# head(design)
# 设置比较组写在前面的分组为enrich表明第一个分组含量高
# ?limma::makeContrasts
group = paste(Desep_group[1],Desep_group[2],sep = "-")
group
BvsA <- limma::makeContrasts(contrasts = group,levels=c( as.character(levels(as.factor(sub_design$Group)))) )#注意是以GF1为对照做的比较
# 组间比较,统计Fold change, Pvalue
lrt = edgeR::glmLRT(fit,contrast=BvsA)
# FDR检验，控制假阳性率小于5%
de_lrt = edgeR::decideTestsDGE(lrt, adjust.method="fdr", p.value=pvalue,lfc=lfc)#lfc=0这个是默认值
summary(de_lrt)
# 导出计算结果
x=lrt$table
x$sig=de_lrt
head(x)
#------差异结果符合otu表格的顺序
row.names(count)[1:6]
x <- cbind(x, padj = p.adjust(x$PValue, method = "fdr"))
enriched = row.names(subset(x,sig==1))
depleted = row.names(subset(x,sig==-1))
x$level = as.factor(ifelse(as.vector(x$sig) ==1, "enriched",ifelse(as.vector(x$sig)==-1, "depleted","nosig")))
x = data.frame(row.names = row.names(x),logFC = x$logFC,level = x$level,p = x$PValue)
head(x)
# colnames(x) = paste(group,colnames(x),sep = "")
# x = res
# head(x)
#------差异结果符合otu表格的顺序
# x = data.frame(row.names = row.names(x),logFC = x$log2FoldChange,level = x$level,p = x$pvalue)
x1 = x %>%
dplyr::filter(level %in% c("enriched","depleted","nosig") )
head(x1)
x1$Genus = row.names(x1)
# x$level = factor(x$level,levels = c("enriched","depleted","nosig"))
if (nrow(x1)<= 1) {
}
x2 <- x1 %>%
dplyr::mutate(ord = logFC^2) %>%
dplyr::filter(level != "nosig") %>%
dplyr::arrange(desc(ord)) %>%
head(n = 5)
file = paste(path,"/",group,j,"_","Edger_Volcano_Top5.csv",sep = "")
write.csv(x2,file,quote = F)
head(x2)
p <- ggplot(x1,aes(x =logFC ,y = -log2(p), colour=level)) +
geom_point() +
geom_hline(yintercept=-log10(0.2),
linetype=4,
color = 'black',
size = 0.5) +
geom_vline(xintercept=c(-1,1),
linetype=3,
color = 'black',
size = 0.5) +
ggrepel::geom_text_repel(data=x2, aes(x =logFC ,y = -log2(p), label=Genus), size=1) +
scale_color_manual(values = c('blue2','red2', 'gray30')) +
ggtitle(group) + theme_bw()
p
file = paste(path,"/",group,j,"_","Edger_Volcano.pdf",sep = "")
ggsave(file,p,width = 8,height = 6)
file = paste(path,"/",group,j,"_","Edger_Volcano.png",sep = "")
ggsave(file,p,width = 8,height = 6)
colnames(x) = paste(group,colnames(x),sep = "")
if (i ==1) {
table =x
}
if (i != 1) {
table = cbind(table,x)
}
}
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"
x = table
###########添加物种丰度#----------
# dim(count)
# str(count)
count = as.matrix(count)
norm = t(t(count)/colSums(count)) #* 100 # normalization to total 100
dim(norm)
#> [1] 39 18
norm1 = norm %>%
t() %>% as.data.frame()
# head(norm1)
#数据分组计算平均值
library("tidyverse")
head(norm1)
iris.split <- split(norm1,as.factor(sub_design$SampleType))
iris.apply <- lapply(iris.split,function(x)colMeans(x))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
norm2= t(iris.combine)
#head(norm)
str(norm2)
#> num [1:39, 1:3] 1.56e-01 3.58e-02 2.84e-06 8.69e-03 6.02e-02 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:39] "Acidobacteria" "Actinobacteria" "Aminicenantes" "Armatimonadetes" ...
#> ..$ : chr [1:3] "Group1" "Group2" "Group3"
norm2 = as.data.frame(norm2)
# dim(x)
head(norm2) x = cbind(x,norm2)
head(x) #在加入这个文件taxonomy时，去除后面两列不相干的列
# 读取taxonomy，并添加各列名称
if (!is.null(ps@tax_table)) {
taxonomy = as.data.frame(ggClusterNet::vegan_tax(ps))
head(taxonomy)
# taxonomy <- as.data.frame(tax_table(ps1))
#发现这个注释文件并不适用于直接作图。
#采用excel将其分列处理，并且删去最后一列，才可以运行
if (length(colnames(taxonomy)) == 6) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
}else if (length(colnames(taxonomy)) == 7) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species")
}else if (length(colnames(taxonomy)) == 8) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species","rep")
}
# colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
# Taxonomy排序，并筛选OTU表中存在的
library(dplyr)
taxonomy$id=rownames(taxonomy)
# head(taxonomy)
tax = taxonomy[row.names(x),]
x = x[rownames(tax), ] # reorder according to tax
if (length(colnames(taxonomy)) == 7) {
x = x[rownames(tax), ] # reorder according to tax
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
# x$species = gsub("","",tax$species,perl=TRUE)
}else if (length(colnames(taxonomy)) == 8) {
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
x$species = gsub("","",tax$species,perl=TRUE)
}else if (length(colnames(taxonomy)) == 9) {
x = x[rownames(tax), ] # reorder according to tax
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
x$species = gsub("","",tax$species,perl=TRUE)
}
} else {
x = cbind(x,tax)
}
res = x
head(res) filename = paste(diffpath.2,"/","_",j,"_","edger_all.csv",sep = "")
write.csv(res,filename)
j = "Genus"
group = "Group"
pvalue = 0.05
artGroup = NULL
path = diffpath
# ps = ps %>%
# ggClusterNet::tax_glom_wt(ranks = j)
if (j %in% c("OTU","gene","meta")) {
ps = ps
} else if (j %in% c(1:7)) {
ps = ps %>%
ggClusterNet::tax_glom_wt(ranks = j)
} else if (j %in% c("Kingdom","Phylum","Class","Order","Family","Genus","Species")){
} else {
ps = ps
print("unknown j, checked please")
}
#> NULL
Desep_group <- ps %>%
phyloseq::sample_data() %>%
.$Group %>%
as.factor() %>%
levels() %>%
as.character()
if ( is.null(artGroup)) {
#--构造两两组合#-----
aaa = combn(Desep_group,2)
# sub_design <- as.data.frame(sample_data(ps))
} else if (!is.null(artGroup)) {
aaa = as.matrix(b )
}
count <- ps %>%
ggClusterNet::vegan_otu() %>% round(0) %>%
t()
map = ps %>%
phyloseq::sample_data() %>%
as.tibble() %>%
as.data.frame()
dds <- DESeq2::DESeqDataSetFromMatrix(countData = count,
colData = map,
design = ~ Group)
dds2 <- DESeq2::DESeq(dds) ##第二步,标准化
# resultsNames(dds2)
#------------根据分组提取需要的差异结果#------------
for (i in 1:dim(aaa)[2]) {
# i = 1
Desep_group = aaa[,i]
print( Desep_group)
# head(design)
# 设置比较组写在前面的分组为enrich表明第一个分组含量高
group = paste(Desep_group[1],Desep_group[2],sep = "-")
group
# 将结果用results()函数来获取，赋值给res变量
res <- DESeq2::results(dds2, contrast=c("Group",Desep_group ),alpha=0.05)
# 导出计算结果
x = res
head(x)
#------差异结果符合otu表格的顺序
x$level = as.factor(ifelse(as.vector(x$padj) < 0.05 & x$log2FoldChange > 0, "enriched",
ifelse(as.vector(x$padj) < 0.05 &x$log2FoldChange < 0, "depleted","nosig")))
x = data.frame(row.names = row.names(x),logFC = x$log2FoldChange,level = x$level,p = x$pvalue)
x1 = x %>%
filter(level %in% c("enriched","depleted","nosig") )
head(x1)
x1$Genus = row.names(x1)
# x$level = factor(x$level,levels = c("enriched","depleted","nosig"))
x2 <- x1 %>%
dplyr::mutate(ord = logFC^2) %>%
dplyr::filter(level != "nosig") %>%
dplyr::arrange(desc(ord)) %>%
head(n = 5)
file = paste(path,"/",group,j,"_","DESep2_Volcano_Top5.csv",sep = "")
write.csv(x2,file,quote = F)
head(x2)
p <- ggplot(x1,aes(x =logFC ,y = -log2(p), colour=level)) +
geom_point() +
geom_hline(yintercept=-log10(0.2),
linetype=4,
color = 'black',
size = 0.5) +
geom_vline(xintercept=c(-1,1),
linetype=3,
color = 'black',
size = 0.5) +
ggrepel::geom_text_repel(data=x2, aes(x =logFC ,y = -log2(p), label=Genus), size=1) +
scale_color_manual(values = c('blue2','red2', 'gray30')) +
ggtitle(group) + theme_bw()
p
file = paste(path,"/",group,j,"_","DESep2_Volcano.pdf",sep = "")
ggsave(file,p,width = 8,height = 6)
file = paste(path,"/",group,j,"_","DESep2_Volcano.png",sep = "")
ggsave(file,p,width = 8,height = 6)
colnames(x) = paste(group,colnames(x),sep = "")
if (i ==1) {
table =x
}
if (i != 1) {
table = cbind(table,x)
}
}
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"
count = as.matrix(count)
norm = t(t(count)/colSums(count)) #* 100 # normalization to total 100
dim(norm)
#> [1] 39 18
norm1 = norm %>%
t() %>% as.data.frame()
# head(norm1)
#数据分组计算平均值
# library("tidyverse")
head(norm1)
iris.split <- split(norm1,as.factor(map$Group))
iris.apply <- lapply(iris.split,function(x)colMeans(x))
# 组合结果
iris.combine <- do.call(rbind,iris.apply)
norm2= t(iris.combine)
#head(norm)
str(norm2)
#> num [1:39, 1:3] 1.56e-01 3.58e-02 2.84e-06 8.69e-03 6.02e-02 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:39] "Acidobacteria" "Actinobacteria" "Aminicenantes" "Armatimonadetes" ...
#> ..$ : chr [1:3] "Group1" "Group2" "Group3"
norm2 = as.data.frame(norm2)
head(norm2) x = cbind(table,norm2)
head(x) #在加入这个文件taxonomy时，去除后面两列不相干的列
# 读取taxonomy，并添加各列名称
if (!is.null(ps@tax_table)) {
taxonomy = as.data.frame(ggClusterNet::vegan_tax(ps))
head(taxonomy)
# taxonomy <- as.data.frame(tax_table(ps1))
#发现这个注释文件并不适用于直接作图。
#采用excel将其分列处理，并且删去最后一列，才可以运行
if (length(colnames(taxonomy)) == 6) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
}else if (length(colnames(taxonomy)) == 7) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species")
}else if (length(colnames(taxonomy)) == 8) {
colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus","species","rep")
}
# colnames(taxonomy) = c("kingdom","phylum","class","order","family","genus")
# Taxonomy排序，并筛选OTU表中存在的
library(dplyr)
taxonomy$id=rownames(taxonomy)
# head(taxonomy)
tax = taxonomy[row.names(x),]
x = x[rownames(tax), ] # reorder according to tax
if (length(colnames(taxonomy)) == 7) {
x = x[rownames(tax), ] # reorder according to tax
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
# x$species = gsub("","",tax$species,perl=TRUE)
}else if (length(colnames(taxonomy)) == 8) {
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
x$species = gsub("","",tax$species,perl=TRUE)
}else if (length(colnames(taxonomy)) == 9) {
x = x[rownames(tax), ] # reorder according to tax
x$phylum = gsub("","",tax$phylum,perl=TRUE)
x$class = gsub("","",tax$class,perl=TRUE)
x$order = gsub("","",tax$order,perl=TRUE)
x$family = gsub("","",tax$family,perl=TRUE)
x$genus = gsub("","",tax$genus,perl=TRUE)
x$species = gsub("","",tax$species,perl=TRUE)
}
} else {
x = x
}
res = x
head(res)
filename = paste(diffpath.1,"/","_",j,"_","DESep2_all.csv",sep = "")
write.csv(res,filename,quote = F)edgeR-Manhattan diagram（edgeR-曼哈顿图）
diffpath = paste(otupath,"/diff_Manhattan/",sep = "")
dir.create(diffpath)
ps = ps
pvalue = 0.05
lfc = 0
diffpath = diffpath
count = ps %>%
ggClusterNet::vegan_otu() %>%
t()
# create DGE list
d = edgeR::DGEList(counts=count, group= phyloseq::sample_data(ps)$Group)
d = edgeR::calcNormFactors(d)
design.mat = model.matrix(~ 0 + d$samples$group)
colnames(design.mat)=levels(as.factor(phyloseq::sample_data(ps)$Group))
d2 = edgeR::estimateGLMCommonDisp(d, design.mat)
d2 = edgeR::estimateGLMTagwiseDisp(d2, design.mat)
fit = edgeR::glmFit(d2, design.mat)
Desep_group <- as.character(levels(as.factor(phyloseq::sample_data(ps)$Group)))
Desep_group
#> [1] "Group1" "Group2" "Group3"
aaa = combn(Desep_group,2)
for (i in 1:dim(aaa)[2]) {
# i = 1
Desep_group = aaa[,i]
print( Desep_group)
group = paste(Desep_group[1],Desep_group[2],sep = "-")
group
BvsA <- limma::makeContrasts(contrasts = group,levels=design.mat)#注意是以GF1为对照做的比较
# 组间比较,统计Fold change, Pvalue
lrt = edgeR::glmLRT(fit,contrast=BvsA)
# FDR检验，控制假阳性率小于5%
de_lrt = edgeR::decideTestsDGE(lrt, adjust.method="fdr", p.value=pvalue,lfc=lfc)#lfc=0这个是默认值
summary(de_lrt)
# 导出计算结果
x=lrt$table
x$sig=de_lrt
x$sig=de_lrt
head(x)
#------差异结果符合otu表格的顺序
row.names(count)[1:6]
x <- cbind(x, padj = p.adjust(x$PValue, method = "fdr"))
enriched = row.names(subset(x,sig==1))
depleted = row.names(subset(x,sig==-1))
x$level = as.factor(ifelse(as.vector(x$sig) ==1, "enriched",ifelse(as.vector(x$sig)==-1, "depleted","nosig")))
x$otu = rownames(x)
x$neglogp = -log(x$PValue)
tax = ps %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
head(tax)
x = cbind(x,tax)
head(x)
top_phylum=c("Bacteroidetes","Firmicutes","Planctomycetes","Proteobacteria","Verrucomicrobia")
x[!(x$Phylum %in% top_phylum),]$Phylum = "Low Abundance" # no level can get value
x$otu = factor(x$otu, levels=x$otu) # set x order
x$level = factor(x$level, levels=c("enriched","depleted","nosig"))
levels(x$Phylum)=c(top_phylum,"Low Abundance")
x = x %>% arrange(Phylum)
head(x)
x$otu = factor(x$otu, levels=x$otu)
# if (tem != 0) {
# tem = x[x$neglogp>15,] %>% nrow()
# }
FDR = min(x$neglogp[x$level=="depleted"])
p = ggplot(x, aes(x=otu, y=neglogp, color=Phylum, size=logCPM, shape=level)) +
geom_point(alpha=.7) +
geom_hline(yintercept=FDR, linetype=2, color="lightgrey") +
scale_shape_manual(values=c(17, 25, 20))+
scale_size(breaks=c(5, 10, 15)) +
labs(x="OTU", y="-loge(P)") +
theme(axis.ticks.x=element_blank(),axis.text.x=element_blank(),legend.position="top") +
scale_color_manual(values = c(RColorBrewer::brewer.pal(9,"Set1")))
p
filename = paste(diffpath,"/",paste(Desep_group[1],Desep_group[2],sep = "_"),"Manhattan_plot.pdf",sep = "")
ggsave(filename,p,width = 16,height = 6)
filename = paste(diffpath,"/",paste(Desep_group[1],Desep_group[2],sep = "_"),"Manhattan_plot.png",sep = "")
ggsave(filename,p,width = 16,height = 6,dpi = 72)
}
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"STAMP_difference analysis（STAMP_差异分析）
ps = readRDS("./data/dataNEW/ps_16s.rds")
diffpath = paste(otupath,"/stemp_diff/",sep = "")
dir.create(diffpath)
allgroup <- combn(unique(map$Group),2)
ps_sub <- phyloseq::subset_samples(ps,Group %in% allgroup[,1]);ps_sub
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 37178 taxa and 12 samples ]
#> sample_data() Sample Data: [ 12 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 37178 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 37178 tips and 37176 internal nodes ]
#> refseq() DNAStringSet: [ 37178 reference sequences ]
Top = 20
ranks = 6
method = "TMM"
test.method = "t.test"
#--门水平合并
data = ggClusterNet::tax_glom_wt(ps_sub,ranks = phyloseq::rank_names(ps)[ranks]) %>%
ggClusterNet::scale_micro(method = method) %>%
ggClusterNet::filter_OTU_ps(Top = 200) %>%
ggClusterNet::vegan_otu() %>%
as.data.frame()
tem = colnames(data)
data$ID = row.names(data)
data <- data %>%
dplyr::inner_join(as.tibble(phyloseq::sample_data(ps)),by = "ID")
data$Group = as.factor(data$Group)
if (test.method == "t.test") {
diff <- data[,tem] %>%
# dplyr::select_if(is.numeric) %>%
purrr::map_df(~ broom::tidy(t.test(. ~ Group,data = data)), .id = 'var')
} else if(test.method == "wilcox.test"){
diff <- data[,tem] %>%
# dplyr::select_if(is.numeric) %>%
purrr::map_df(~ broom::tidy(wilcox.test(. ~ Group,data = data)), .id = 'var')
}
diff$p.value[is.nan(diff$p.value)] = 1
diff$p.value <- p.adjust(diff$p.value,"bonferroni")
tem = diff$p.value [diff$p.value < 0.05] %>% length()
if (tem > 30) {
diff <- diff %>%
dplyr::filter(p.value < 0.05) %>%
head(30)
} else {
diff <- diff %>%
# filter(p.value < 0.05) %>%
head(30)
}
# diff <- diff %>% filter(p.value < 0.05)
# diff1$p.value <- p.adjust(diff1$p.value,"bonferroni")
# diff1 <- diff1 %>% filter(p.value < 0.05)
abun.bar <- data[,c(diff$var,"Group")] %>%
tidyr::gather(variable,value,-Group) %>%
dplyr::group_by(variable,Group) %>%
dplyr::summarise(Mean = mean(value))
diff.mean <- diff[,c("var","estimate","conf.low","conf.high","p.value")]
diff.mean$Group <- c(ifelse(diff.mean$estimate >0,levels(data$Group)[1],
levels(data$Group)[2]))
diff.mean <- diff.mean[order(diff.mean$estimate,decreasing = TRUE),]
cbbPalette <- c("#E69F00", "#56B4E9")
abun.bar$variable <- factor(abun.bar$variable,levels = rev(diff.mean$var))
p1 <- ggplot(abun.bar,aes(variable,Mean,fill = Group)) +
scale_x_discrete(limits = levels(diff.mean$var)) +
coord_flip() +
xlab("") +
ylab("Mean proportion (%)") +
theme(panel.background = element_rect(fill = 'transparent'),
panel.grid = element_blank(),
axis.ticks.length = unit(0.4,"lines"),
axis.ticks = element_line(color='black'),
axis.line = element_line(colour = "black"),
axis.title.x=element_text(colour='black', size=12,face = "bold"),
axis.text=element_text(colour='black',size=10,face = "bold"),
legend.title=element_blank(),
# legend.text=element_text(size=12,face = "bold",colour = "black",
# margin = margin(r = 20)),
legend.position = c(-1,-0.1),
legend.direction = "horizontal",
legend.key.width = unit(0.8,"cm"),
legend.key.height = unit(0.5,"cm"))
p1
for (i in 1:(nrow(diff.mean) - 1))
p1 <- p1 + annotate('rect', xmin = i+0.5, xmax = i+1.5, ymin = -Inf, ymax = Inf,
fill = ifelse(i %% 2 == 0, 'white', 'gray95'))
p1
p1 <- p1 +
geom_bar(stat = "identity",position = "dodge",width = 0.7,colour = "black") +
scale_fill_manual(values=cbbPalette) + theme(legend.position = "bottom")
p1
diff.mean$var <- factor(diff.mean$var,levels = levels(abun.bar$variable))
diff.mean$p.value <- signif(diff.mean$p.value,3)
diff.mean$p.value <- as.character(diff.mean$p.value)
p2 <- ggplot(diff.mean,aes(var,estimate,fill = Group)) +
theme(panel.background = element_rect(fill = 'transparent'),
panel.grid = element_blank(),
axis.ticks.length = unit(0.4,"lines"),
axis.ticks = element_line(color='black'),
axis.line = element_line(colour = "black"),
axis.title.x=element_text(colour='black', size=12,face = "bold"),
axis.text=element_text(colour='black',size=10,face = "bold"),
axis.text.y = element_blank(),
legend.position = "none",
axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(size = 15,face = "bold",colour = "black",hjust = 0.5)) +
scale_x_discrete(limits = levels(diff.mean$var)) +
coord_flip() +
xlab("") +
ylab("Difference in mean proportions (%)") +
labs(title="95% confidence intervals")
for (i in 1:(nrow(diff.mean) - 1))
p2 <- p2 + annotate('rect', xmin = i+0.5, xmax = i+1.5, ymin = -Inf, ymax = Inf,
fill = ifelse(i %% 2 == 0, 'white', 'gray95'))
p2 <- p2 +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high),
position = position_dodge(0.8), width = 0.5, size = 0.5) +
geom_point(shape = 21,size = 3) +
scale_fill_manual(values=cbbPalette) +
geom_hline(aes(yintercept = 0), linetype = 'dashed', color = 'black')
p3 <- ggplot(diff.mean,aes(var,estimate,fill = Group)) +
geom_text(aes(y = 0,x = var),label = diff.mean$p.value,
hjust = 0,fontface = "bold",inherit.aes = FALSE,size = 3) +
geom_text(aes(x = nrow(diff.mean)/2 +0.5,y = 0.85),label = "P-value (corrected)",
srt = 90,fontface = "bold",size = 5) +
coord_flip() +
ylim(c(0,1)) +
theme(panel.background = element_blank(),
panel.grid = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank())
library(patchwork)
p <- p1 + p2 + p3 + plot_layout(widths = c(4,6,2))
p
i = 1
# filename = paste(diffpath,"/",paste(allgroup[,i][1],allgroup[,i][2],sep = "_"),"stemp_P_plot.csv",sep = "")
# write.csv(diff.mean,filename)
filename = paste(diffpath,"/",paste(allgroup[,i][1],
allgroup[,i][2],sep = "_"),phyloseq::rank.names(ps)[j],"stemp_plot.pdf",sep = "")
ggsave(filename,p,width = 14,height = 6)
filename = paste(diffpath,"/",paste(allgroup[,i][1],
allgroup[,i][2],sep = "_"),phyloseq::rank.names(ps)[j],"stemp_plot.jpg",sep = "")
ggsave(filename,p,width = 14,height = 6)
detach("package:patchwork")Heatmap + Bubble diagram （热图+气泡图）
ps = readRDS("./data/dataNEW/ps_16s.rds")
heatpath = paste(otupath,"/heapmap_boplot/",sep = "")
dir.create(heatpath)
map = phyloseq::sample_data(ps)
map$ID = row.names(map)
phyloseq::sample_data(ps) = map
j = 2
ps_tem = ps %>%
ggClusterNet::scale_micro(method = "TMM") %>%
ggClusterNet::tax_glom_wt(ranks = j)
rowSD = function(x){
apply(x,1, sd)
}
rowCV = function(x){
rowSD(x)/rowMeans(x)
}
id <- ps %>%
ggClusterNet::scale_micro(method = "TMM") %>%
ggClusterNet::tax_glom_wt(ranks = j) %>%
ggClusterNet::filter_OTU_ps(100) %>%
ggClusterNet::vegan_otu() %>%
t() %>% as.data.frame() %>%rowCV %>%
sort(decreasing = TRUE) %>%
head(20) %>%
names()
ps_rela= ps_tem
heatnum = 20
label=TRUE
col_cluster=TRUE
row_cluster=TRUE
map = phyloseq::sample_data(ps_rela)
map$ID = row.names(map)
phyloseq::sample_data(ps_rela) = map
otu = as.data.frame(t(ggClusterNet::vegan_otu(ps_rela)))
otu = as.matrix(otu[id,])
ps_heatm = phyloseq::phyloseq(
phyloseq::otu_table(otu,taxa_are_rows = TRUE),
phyloseq::tax_table(ps_rela),
phyloseq::sample_data(ps_rela)
)
# print(ps_heatm)
datah <- as.data.frame(t(ggClusterNet::vegan_otu(ps_heatm)))
head(datah) tax = as.data.frame(ggClusterNet::vegan_tax(ps_heatm))
otutaxh = cbind(datah,tax)
head(otutaxh)
otutaxh$id = paste(row.names(otutaxh),otutaxh$Genus,sep = "_")
# otutaxh$id = row.names(otutaxh)
row.names(otutaxh) = otutaxh$id
data <- otutaxh[,c("id",phyloseq::sample_names(ps))]
rig <- data[,phyloseq::sample_names(ps)] %>% rowMeans() %>% as.data.frame()
head(rig) colnames(rig) = "MeanAbundance"
rig$id = row.names(rig)
rig = rig %>% dplyr::arrange(MeanAbundance)
rig$id = factor(rig$id,levels = rig$id)
p_rig = ggplot(rig) + geom_bar(aes(y = id,x = MeanAbundance),
fill = "#A54657",
stat = "identity") + theme_void()
tem = data[,phyloseq::sample_names(ps)] %>% as.matrix()
tem = scale(t(tem)) %>% t() %>%
as.data.frame()
data[,phyloseq::sample_names(ps)] = tem
# data[data > 0.3]<-0.3
mat <- data[,-1] #drop gene column as now in rows
if (col_cluster == TRUE) {
clust <- hclust(dist(mat %>% as.matrix())) # hclust with distance matrix
ggtree_plot <- ggtree::ggtree(clust)
}
if (row_cluster == TRUE) {
v_clust <- hclust(dist(mat %>% as.matrix() %>% t()))
ggtree_plot_col <- ggtree::ggtree(v_clust) + ggtree::layout_dendrogram()
}
if (label == TRUE) {
map = as.data.frame(phyloseq::sample_data(ps))
map$ID = row.names(map)
labels= ggplot(map, aes(x = ID, y=1, fill=Group)) + geom_tile() +
scale_fill_brewer(palette = 'Set1',name="Cell Type") +
theme_void()
}
map = phyloseq::sample_data(ps) %>% as.tibble() %>%
dplyr::arrange(Group) %>% as.data.frame()
map$ID
#> [1] "sample1" "sample2" "sample3" "sample4" "sample5" "sample6"
#> [7] "sample10" "sample11" "sample12" "sample7" "sample8" "sample9"
#> [13] "sample13" "sample14" "sample15" "sample16" "sample17" "sample18"
pcm = reshape2::melt(data, id = c("id"))
pcm$variable = factor(pcm$variable,levels = map$ID)
pcm$id = factor(pcm$id,levels = rig$id)
p1 = ggplot(pcm, aes(y = id, x = variable)) +
# geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) +
geom_tile(aes(size = value,fill = value))+
scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1)) +
labs( y= "", x = "", size = "Relative Abundance (%)", fill = "") +
# scale_fill_manual(values = colours, guide = FALSE) +
scale_x_discrete(limits = rev(levels(pcm$variable))) +
scale_y_discrete(position = "right") +
scale_fill_gradientn(colours =colorRampPalette(RColorBrewer::brewer.pal(11,"Spectral")[11:1])(60))+
theme(
panel.background=element_blank(),
panel.grid=element_blank(),
axis.text.x = element_text(colour = "black",angle = 90)
)
colours = c( "#A54657", "#582630", "#F7EE7F", "#4DAA57","#F1A66A","#F26157", "#F9ECCC", "#679289", "#33658A",
"#F6AE2D","#86BBD8")
#----样本在y轴上
p2 = ggplot(pcm, aes(y = id, x = variable)) +
geom_point(aes(size = value,fill = value), alpha = 0.75, shape = 21) +
scale_size_continuous(limits = c(0.000001, 100), range = c(2,25), breaks = c(0.1,0.5,1)) +
labs( y= "", x = "", size = "Relative Abundance (%)", fill = "") +
# scale_fill_manual(values = colours, guide = FALSE) +
scale_x_discrete(limits = rev(levels(pcm$variable))) +
scale_y_discrete(position = "right") +
scale_fill_gradientn(colours =colorRampPalette(RColorBrewer::brewer.pal(11,"Spectral")[11:1])(60)) +
theme(
panel.background=element_blank(),
panel.grid=element_blank(),
axis.text.x = element_text(colour = "black",angle = 90)
)
p1 <- p1 %>%
aplot::insert_right(p_rig, width=.2)
p2 <- p2 %>%
aplot::insert_right(p_rig, width=.2)
if (col_cluster == TRUE) {
p1 <- p1 %>%
aplot::insert_left(ggtree_plot, width=.2)
p2 <- p2 %>%
aplot::insert_left(ggtree_plot, width=.2)
}
if (row_cluster == TRUE) {
p1 <- p1 %>%
aplot::insert_top(labels, height=.02)
p2 <- p2 %>%
aplot::insert_top(labels, height=.02)
}
if (label == TRUE) {
p1 <- p1 %>%
aplot::insert_top(ggtree_plot_col, height=.1)
p2 <- p2 %>%
aplot::insert_top(ggtree_plot_col, height=.1)
}
filename = paste(heatpath,"/",phyloseq::rank.names(ps)[j],"Topggheatmap.pdf",sep = "")
ggsave(filename,p1,width = 14,height = (6 + heatnum/10))
filename = paste(heatpath,phyloseq::rank.names(ps)[j],"Topggbubble.pdf",sep = "")
ggsave(filename,p2,width = 14,height = (6 + heatnum/10))
filename = paste(heatpath,"/",phyloseq::rank.names(ps)[j],"Topggheatmap.png",sep = "")
ggsave(filename,p1,width = 14,height = (6 + heatnum/10))
filename = paste(heatpath,phyloseq::rank.names(ps)[j],"Topggbubble.png",sep = "")
ggsave(filename,p2,width = 14,height = (6 + heatnum/10))
Multi-group difference analysis volcano plot（多组差异分析火山图）
# BiocManager::install("MetBrewer")
diffpath.1 = paste(otupath,"/Mui.Group.v/",sep = "")
dir.create(diffpath.1)
source("./function/EdgerSuper2.R")
res = EdgerSuper2 (ps = ps,group = "Group",artGroup =NULL,
j = "OTU",
path = diffpath.1
)
#> [1] "Group1" "Group2"
#> [1] "Group1" "Group3"
#> [1] "Group2" "Group3"
head(res)
res$ID = row.names(res)
datv = res
# for循环挑选每个cluster的top前5 gene symbol
tm.g <- function(data){
id = data$group %>% unique()
for (i in 1:length(id)) {
tem = filter(data,group==id[i],level != "nosig") %>%
distinct(ID,.keep_all = TRUE) %>%
top_n(5,abs(logFC))
if (i == 1) {
tem2 = tem
} else {
tem2 = rbind(tem2,tem)
}
}
return(tem2)
}
top <- tm.g(datv)
# 先画背景柱，根据数据log2FC的max值,min值来确定
#根据数据中log2FC区间确定背景柱长度：
head(datv)
tem = datv %>% group_by(group) %>% summarise(max = max(logFC),min = min(logFC)) %>% as.data.frame()
col1<-data.frame(x=tem$group,
y=tem$max)
col2<-data.frame(x=tem$group,
y=tem$min)
# 绘制背景柱
p1 <- ggplot()+
geom_col(data = col1,
mapping = aes(x = x,y = y),
fill = "#dcdcdc",alpha = 0.6)+
geom_col(data = col2,
mapping = aes(x = x,y = y),
fill = "#dcdcdc",alpha = 0.6)
p1
#把散点火山图叠加到背景柱上：
head(datv)
p2 <- ggplot()+
geom_col(data = col1,
mapping = aes(x = x,y = y),
fill = "#dcdcdc",alpha = 0.6)+
geom_col(data = col2,
mapping = aes(x = x,y = y),
fill = "#dcdcdc",alpha = 0.6)+
geom_jitter(data = datv,
aes(x =group , y = logFC, color =level ),
size = 1,
width =0.4)+
scale_color_manual(name=NULL,
values = c("#4393C3","#FC4E2A","grey40"))+
labs(x="",y="log2(FoldChange)")
p2
# 添加X轴的分组色块标签：
dfcol<-data.frame(x=tem$group,
y=0,
label=tem$group)
# 添加分组色块标签
dfcol$group <- tem$group
# 加载包
library(RColorBrewer)
library(MetBrewer)
# BiocManager::install("MetBrewer")
# 自定义分组色块的颜色
tile_color <- met.brewer("Thomas",length(tem$group))
# 在图中镶嵌色块
p3 <- p2 + geom_tile(data = dfcol,
aes(x=x,y=y),
height=1.75,
color = "black",
fill = tile_color,
alpha = 0.6,
show.legend = F)+
geom_text(data=dfcol,
aes(x=x,y=y,label=group),
size =3.5,
color ="white") + theme_classic()
p3
library(ggrepel)
p4<-p3+geom_text_repel(
data=top,
aes(x=group,y=logFC,label=ID),
force = 1.2,
arrow = arrow(length = unit(0.008, "npc"),
type = "open", ends = "last"))
p4
# 去除背景，美化图片
p5 <- p4+
theme_minimal()+
theme(
axis.title = element_text(size = 18,
color = "black",
face = "bold"),
axis.line.y = element_line(color = "black",
size = 1.2),
axis.line.x = element_blank(),
axis.text.x = element_blank(),
panel.grid = element_blank(),
legend.position = "top",
legend.direction = "vertical",
legend.justification = c(1,0),
legend.text = element_text(size = 12)
)
p5
# return(list(p5,p3,datv,top))
p = p3
p
filename = paste(diffpath.1,"/","Mui.group.volcano.pdf",sep = "")
ggsave(filename,p,width = 12,height = 6,limitsize = FALSE)3.Biomarker identification （3.生物标志物鉴别）
LEfSe analysis（LEfSe分析）
lefsepath = paste(otupath,"/lefse_R_plot/",sep = "")
dir.create(lefsepath)
ps = readRDS("./data/dataNEW/ps_16s.rds")
library(ggtree)
p_base = function(ps,Top = 100,ranks = 6) {
alltax = ps %>%
ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
alltax$OTU = row.names(alltax)
alltax$Kingdom = paste(alltax$Kingdom,sep = "_Rank_")
for (i in 2:ranks) {
alltax[,i]= paste(alltax[,i - 1],alltax[,i],sep = "_Rank_")
}
alltax[is.na(alltax)] = "Unknown"
trda <- MicrobiotaProcess::convert_to_treedata(alltax)
p <- ggtree(trda, layout="circular", size=0.2, xlim=c(30,NA)) +
geom_point(
pch = 21,
size=3,
alpha=1,
fill = "#FFFFB3"
)
p$data$lab2 <- p$data$label %>% strsplit( "_Rank_") %>%
sapply(
function(x) x[length(x)]
)
p$data$lab2 = gsub("st__","",p$data$lab2 )
p$data$nodeSize = 1
return(p)
}
LDA_Micro = function(ps = ps,
Top = 100,
ranks = 6,
p.lvl = 0.05,
lda.lvl = 2,
seed = 11,
adjust.p = F
){
alltax = ps %>%
ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
phyloseq::filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
alltax$OTU = row.names(alltax)
alltax$Kingdom = paste(alltax$Kingdom,sep = "_Rank_")
for (i in 2:ranks) {
alltax[,i]= paste(alltax[,i - 1],alltax[,i],sep = "_Rank_")
}
otu = ps %>%
ggClusterNet::tax_glom_wt(ranks = ranks ) %>%
phyloseq::filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
ggClusterNet::filter_OTU_ps(Top) %>%
ggClusterNet::vegan_otu() %>%
t() %>%
as.data.frame()
otu_tax = merge(otu,alltax,by = "row.names",all = F)
head(otu_tax)
tem = colnames(alltax)[-length(colnames(alltax))]
i = 1
tem2 = c("k__","p__","c__","o__","f__","g__","s__","st__")
for (i in 1:ranks) {
rank1 <- otu_tax %>%
dplyr::group_by(!!sym(tem[i])) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank1)[1] = "id"
rank1$id = paste(tem2[i],rank1$id,sep = "")
if (i == 1) {
all = rank1
}
if (i != 1) {
all = rbind(all,rank1)
}
}
#--LDA排序#--------
data1 = as.data.frame(all)
row.names(data1) = data1$id
data1$id = NULL
#-构建phylose对象
ps_G_graphlan = phyloseq::phyloseq(phyloseq::otu_table(as.matrix(data1),taxa_are_rows = TRUE),
phyloseq::sample_data(ps))# %>% filter_taxa(function(x) sum(x ) > 0 , TRUE)
ps_G_graphlan
#----提取OTU表格
otu = as.data.frame((ggClusterNet::vegan_otu(ps_G_graphlan)))
map = as.data.frame(phyloseq::sample_data(ps_G_graphlan))
# otu = (otu_table)
claslbl= map$Group %>% as.factor()
set.seed(seed)
#KW rank sum test
rawpvalues <- apply(otu, 2, function(x) kruskal.test(x, claslbl)$p.value);
#--得到计算后得到的p值
ord.inx <- order(rawpvalues)
rawpvalues <- rawpvalues[ord.inx]
clapvalues <- p.adjust(rawpvalues, method ="fdr")
# p.adjust
wil_datadf <- as.data.frame(otu[,ord.inx])
ldares <- MASS::lda(claslbl ~ .,data = wil_datadf)
# ldares
ldamean <- as.data.frame(t(ldares$means))
ldamean
class_no <<- length(unique(claslbl))
ldamean$max <- apply(ldamean[,1:class_no],1,max);
ldamean$min <- apply(ldamean[,1:class_no],1,min);
#---计算LDA
ldamean$LDAscore <- signif(log10(1+abs(ldamean$max-ldamean$min)/2),digits=3);
head(ldamean)
a = rep("A",length(ldamean$max))
for (i in 1:length(ldamean$max)) {
name =colnames(ldamean[,1:class_no])
a[i] = name[ldamean[,1:class_no][i,] %in% ldamean$max[i]]
}
ldamean$class = a
tem1 = row.names(ldamean)
tem1 %>% as.character()
ldamean$Pvalues <- signif(rawpvalues[match(row.names(ldamean),names(rawpvalues))],digits=5)
ldamean$FDR <- signif(clapvalues,digits=5)
resTable <- ldamean
rawNms <- rownames(resTable);
rownames(resTable) <- gsub("`", '', rawNms);
if (adjust.p) {
de.Num <- sum(clapvalues <= p.lvl & ldamean$LDAscore>=lda.lvl)
} else {
de.Num <- sum(rawpvalues <= p.lvl & ldamean$LDAscore>=lda.lvl)
}
if(de.Num == 0){
current.msg <<- "No significant features were identified with given criteria.";
}else{
current.msg <<- paste("A total of", de.Num, "significant features with given criteria.")
}
print(current.msg)
# sort by p value
ord.inx <- order(resTable$Pvalues, resTable$LDAscore)
resTable <- resTable[ord.inx, ,drop=FALSE]
resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))]
resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))]
ldamean$Pvalues[is.na(ldamean$Pvalues)] = 1
# resTable %>% tail()
if (adjust.p) {
taxtree = resTable[clapvalues <=p.lvl & ldamean$LDAscore>=lda.lvl,]
} else {
# taxtree = resTable[ldamean$Pvalues <=p.lvl & ldamean$LDAscore>=lda.lvl,]
taxtree = resTable[ldamean$Pvalues <=p.lvl,]
}
#-提取所需要的颜色
colour = c('darkgreen','red',"blue","#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF")
selececol = colour[1:length(levels(as.factor(taxtree$class)))]
names(selececol) = levels(as.factor(taxtree$class))
A = rep("a",length(row.names(taxtree)))
for (i in 1:length(row.names(taxtree))) {
A[i] = selececol [taxtree$class[i]]
}
taxtree$color = A
# taxtree <- taxtree[row.names(taxtree) != "k__Bacteria",]
# node_ids <- p0$data
# anno <- rep("white", nrow(p1$data))
lefse_lists = data.frame(node=row.names(taxtree),
color=A,
Group = taxtree$class,
stringsAsFactors = FALSE
)
return(list(lefse_lists,taxtree))
}
# gtree = p1
# anno.data= tablda[[1]]
# alpha=0.3
# anno.depth = 2
library(patchwork)
clade.anno_wt <- function(gtree, anno.data, alpha = 0.2, anno.depth = 5, anno.x = 10,
anno.y = 40){
short.labs <- c(letters,paste(letters,1:500,sep = ""))
get_offset <- function(x) {
(x * 0.2 + 0.2)^2
}
get_angle <- function(node) {
data <- gtree$data
sp <- tidytree::offspring(data, node)$node
sp2 <- c(sp, node)
sp.df <- data[match(sp2, data$node), ]
mean(range(sp.df$angle))
}
anno.data <- dplyr::arrange(anno.data, node)
hilight.color <- anno.data$color
node_list <- anno.data$node
node_ids <- (gtree$data %>% filter(label %in% node_list) %>%
arrange(label))$node
anno <- rep("yellow", nrow(gtree$data))
#---添加阴影#-------
i = 1
for (i in 1:length(node_ids)) {
n <- node_ids[i]
color <- hilight.color[i]
anno[n] <- color
mapping <- gtree$data %>% filter(node == n)
nodeClass <- as.numeric(mapping$nodeDepth)
offset <- get_offset(nodeClass)
gtree <- gtree + geom_hilight(node = n, fill = color,
alpha = alpha, extend = offset)
}
# gtree$layers <- rev(gtree$layers)
# gtree <- gtree + geom_point2(aes(size = I(nodeSize)), fill = anno,
# shape = 21)
short.labs.anno <- NULL
i = 1
# gtree$data
#--添加标签#--------
for (i in 1:length(node_ids)) {
n <- node_ids[i]
mapping <- gtree$data %>% filter(node == n)
nodeClass <- as.numeric(mapping$nodeDepth)
if (nodeClass <= anno.depth) {
lab <- short.labs[1]
short.labs <- short.labs[-1]
if (is.null(short.labs.anno)) {
short.labs.anno = data.frame(lab = lab, annot = mapping$lab2,
stringsAsFactors = F)
}else {
short.labs.anno = rbind(short.labs.anno, c(lab,mapping$lab2))
}
} else {
lab <- mapping$lab2
}
offset <- get_offset(nodeClass) - 0.4
angle <- get_angle(n) + 90
gtree <- gtree + geom_cladelabel(node = n, label = lab,
angle = angle, fontsize = 1 + sqrt(nodeClass),
offset = offset, barsize = NA, hjust = 0.5)
}
if (!is.null(short.labs.anno)) {
anno_shapes = sapply(short.labs.anno$lab, utf8ToInt)
stable.p <- ggpubr::ggtexttable(short.labs.anno, rows = NULL,
theme = ggpubr::ttheme(
colnames.style = ggpubr::colnames_style(fill = "white"),
tbody.style = ggpubr::tbody_style(fill = ggpubr::get_palette("RdBu", 6))
))
}
y = (1:length(unique(anno.data$Group)))
pleg <- ggplot() + geom_point2(aes(
y = y,
x = rep(1,length(unique(anno.data$color))),fill = as.factor(1:length(unique(anno.data$Group)))
),pch = 21,size = 2) +
geom_text(aes( y = y,
x = rep(1,length(unique(anno.data$color))),label = unique(anno.data$Group) ),
hjust = -1
) + scale_fill_manual(values = unique(anno.data$color),guide = F) +
theme_void()
layout <- "
AAAAAABB#
AAAAAABB#
AAAAAABBC
AAAAAABBC
AAAAAABB#
"
if (is.null(short.labs.anno)) {
gtree <- gtree + pleg + plot_layout(design = layout)
} else {
gtree <- gtree + stable.p+ pleg + plot_layout(design = layout)
}
}
lefse_bar = function(taxtree = tablda[[2]]){
taxtree = tablda[[2]]
taxtree$ID = row.names(taxtree)
head(taxtree)
taxtree$ID = gsub("_Rank_",";",taxtree$ID)
taxtree <- taxtree %>%
arrange(class,LDAscore)
taxtree$ID = factor(taxtree$ID,levels=taxtree$ID)
taxtree$class = factor(taxtree$class,levels = unique(taxtree$class))
pbar <- ggplot(taxtree) + geom_bar(aes(y =ID, x = LDAscore,fill = class),stat = "identity") +
scale_fill_manual(values = unique(taxtree$color)) + mytheme1 +
scale_x_continuous(limits = c(0,max(taxtree$LDAscore)*1.2))
}
for (j in 2:4) {
p1 <- p_base(ps,Top = 200,ranks =j)
p1
tablda = LDA_Micro(ps = ps,
Top = 200,
ranks = j,
p.lvl = 0.05,
lda.lvl = 2,
seed = 11,
adjust.p = F)
p2 <- clade.anno_wt(p1, tablda[[1]], alpha=0.3,anno.depth = 2)
p2
FileName <- paste(lefsepath,j,"_tree_lefse", ".pdf", sep = "")
ggsave(FileName,p2,width = 15,height = 10)
FileName <- paste(lefsepath,j,"_tree_lefse", ".png", sep = "")
ggsave(FileName,p2,width = 15,height = 10)
p <- lefse_bar(taxtree = tablda[[2]])
FileName <- paste(lefsepath,j,"_bar_lefse", ".pdf", sep = "")
ggsave(FileName, p, width = 15, height =9)
FileName <- paste(lefsepath,j,"_bar_lefse", ".png", sep = "")
ggsave(FileName, p, width = 15, height =9)
res = tablda[[2]]
FileName <- paste(lefsepath,j,"_tree_lefse_data", ".csv", sep = "")
write.csv(res,FileName,quote = F)
}
#> [1] "A total of 5 significant features with given criteria."
#> [1] "A total of 14 significant features with given criteria."
#> [1] "A total of 22 significant features with given criteria."Machine learning（机器学习）
Microbial communities + Science article- Chart combination（微生物群落和环境Science组合图表）
ps = base::readRDS("./data/dataNEW/ps_16s.rds")
env = read.csv("./data/dataNEW/env.csv")
head(env)envRDA = env
head(env)row.names(envRDA) = env$ID
envRDA$ID = NULL
head(envRDA)
compath = paste(res1path,"/Conbine_env_plot/",sep = "")
dir.create(compath)
otu1 = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
head(otu1)
tabOTU1 = list(bac = otu1)
# tabOTU1 = list(b = otu)
# tabOTU1 = list(f = otu2)
p0 <- ggClusterNet:: MatCorPlot(env.dat = envRDA,
tabOTU = tabOTU1,
diag = FALSE,
range = 0.1,
numpoint = 21,
sig = TRUE,
siglabel = FALSE,
shownum = FALSE,
curvature = 0.1,
numsymbol = NULL,
lacx = "left",
lacy = "bottom",
p.thur = 0.05,
onlysig = TRUE
)
p0
p0 <- p0 + scale_colour_manual(values = c("blue","red")) +
scale_fill_distiller(palette="PRGn")
p0
FileName <- paste(compath,"Conbine_envplot", ".pdf", sep = "")
ggsave(FileName, p0,width = 15,height = 10)# , device = cairo_pdf, family = "Song"
FileName <- paste(compath,"Conbine_envplot", ".png", sep = "")
ggsave(FileName, p0,width = 15,height = 10)# , device = cairo_pdf, family = "Song"
#--拆分开来
#--- mantel test
rep = ggClusterNet::MetalTast (env.dat = envRDA, tabOTU = tabOTU1,distance = "bray",method = "metal")
repR = rep[c(-seq(from=1,to=dim(rep)[2],by=2)[-1])]
repP = rep[seq(from=1,to=dim(rep)[2],by=2)]
head( repR)head( repP)mat = cbind(repR,repP)
head(mat)
FileName <- paste(compath,"Conbine_envplot_data", ".csv", sep = "")
write.csv(mat,FileName)
result <- ggClusterNet::Miccorplot(data = envRDA,
method.cor = "spearman",
cor.p = 0.05,
x = F,
y = F,
diag = T,
lacx = "left",
lacy = "bottom",
sig = T,
siglabel = F,
shownum = F,
numpoint = 21,
numsymbol = NULL
)
p1 <- result[[1]]
p1 <- p1 + scale_fill_distiller(palette="PRGn")
p1
FileName <- paste(compath,"envCorplot", ".pdf", sep = "")
ggsave(FileName, p1,width = 15,height = 10)
FileName <- paste(compath,"envCorplot", ".png", sep = "")
ggsave(FileName, p1,width = 15,height = 10)